A speed planning based hybrid vehicle energy optimization control method and system

Abstract

The application provides a kind of hybrid vehicle energy optimization control method and system based on speed planning, comprising the following steps: A, the driving state is classified according to driving purpose, and the data acquisition content of different category speed planning curve is set;B, determine the category of the planned speed curve, carry out speed data acquisition, adopt Gaussian function algorithm to fit the speed curve, carry out speed planning, obtain the target speed planning curve of the vehicle in this period;C, establish a series hybrid electric vehicle (SHEV) model;D, establish a target performance function;E, according to the target speed planning curve, the optimization control sequence of the entire driving process of the target speed planning curve is calculated by using model predictive control algorithm, the engine output power control curve of the driving process is obtained, and the vehicle engine is controlled to run on the economic curve.The application can improve the comfort and accuracy of vehicle automatic driving, make the local optimal energy consumption of vehicle, and improve fuel efficiency.

Description

Technical Field

[0001] This invention relates to the fields of speed planning for autonomous vehicles and energy optimization control management for hybrid vehicles, specifically to a method and system for energy optimization control of hybrid vehicles based on speed planning. Background Technology

[0002] Autonomous vehicles rely on the collaborative efforts of artificial intelligence, computer vision, radar, monitoring devices, and GPS to enable computers to operate motor vehicles automatically and safely without any human intervention.

[0003] While current autonomous driving technology can be applied to various scenarios such as cruising, following, overtaking, and parking, there are some drawbacks. In following or overtaking scenarios, the fitted speed curve exhibits a large peak in longitudinal acceleration, resulting in poor comfort. Furthermore, in parking scenarios, there is a prolonged period at the end of the speed range where acceleration approaches zero, which is detrimental to control and cannot guarantee the accuracy of parking. Therefore, a speed planning method is needed to make autonomous driving more comfortable and precise.

[0004] In hybrid vehicle performance, fuel economy is influenced by vehicle structure, powertrain parameter matching, vehicle parameters, operating cycle conditions, and control strategies. Therefore, it is necessary to further improve the energy management strategies of hybrid vehicles based on existing technologies. Dynamic programming (DP) is a global optimization algorithm that requires prior knowledge of global operating conditions and can obtain a globally optimal solution; however, it involves large computational costs and cannot be run in real-time on the vehicle controller. To better utilize global optimization, finding the optimal control action in each predicted time domain, ensuring optimality and real-time control—i.e., model predictive control (MPC)—an online rolling optimization method and feedback self-tuning strategy are employed to achieve the expected control objectives and ensure good robustness and stability of the system. Summary of the Invention

[0005] The present invention aims to provide a speed planning-based energy optimization control method and system for hybrid vehicles. This method and system can improve the comfort and accuracy of autonomous driving, optimize local energy consumption, and improve fuel efficiency.

[0006] The technical solution of the present invention is as follows:

[0007] The aforementioned energy optimization control method for hybrid vehicles based on speed planning uses a model predictive control algorithm as the optimization algorithm. The upper layer employs a Gaussian function-based algorithm to plan the vehicle speed, while the lower layer primarily manages the vehicle's energy, mainly considering fuel consumption and battery SOC (state of charge) trajectory tracking. The method includes the following steps:

[0008] A. Classify driving conditions according to driving purpose and set the data collection content for different categories of speed planning curves;

[0009] B. Determine which category the proposed speed curve belongs to, collect speed data as the basis for the planned speed curve of this vehicle in this cycle, use the Gaussian function algorithm to fit the speed curve, perform speed planning, and obtain the target speed planning curve of this vehicle in this cycle.

[0010] Among them, the Gaussian function algorithm includes a single-peak Gaussian function and a multi-peak Gaussian function, which are selected according to whether the collected velocity data is a single-peak or multi-peak value;

[0011] C. Establish a series hybrid electric vehicle (SHEV) model, including an engine-generator set, a drive motor, and a battery pack;

[0012] D. Based on the two coordinated optimization objectives of reducing engine fuel consumption and stabilizing battery SOC relative to the reference trajectory, establish the objective performance function;

[0013] E. Based on the target speed planning curve, the model predictive control algorithm is used to solve the target performance function and calculate the optimal control sequence for the entire driving process of the target speed planning curve, thereby obtaining the engine output power control curve for this driving process; based on the engine output power control curve, the vehicle engine is controlled to operate on the economic curve, ensuring the vehicle's power demand while reducing vehicle fuel consumption.

[0014] The driving status classifications are specifically: following vehicle status, overtaking vehicle status, and parking vehicle status.

[0015] In step B, the speed curve planning process is as follows:

[0016] Vehicle following status: The starting point of the planned speed curve is the end point of the planned speed curve in the previous cycle. The collected information includes the speed and acceleration data of the current vehicle at this point, as well as the speed and acceleration data of the vehicle being followed in the previous cycle. An extreme value search is performed on the speed data of the vehicle being followed in the previous cycle to determine whether to use a single-peak or multi-peak Gaussian function algorithm for speed planning in this planning cycle.

[0017] Vehicle overtaking driving status: The starting point of the planned speed curve is the end point of the driving speed curve planned in the previous cycle. The collected information includes the vehicle's speed and acceleration data at this point, as well as the speed and acceleration data of vehicles in front, to the left, to the right, and behind the vehicle collected in the previous cycle; the vehicle in front is the overtaking target; based on the speed Vi of the overtaken vehicle collected in the previous cycle, with speed Vi*(1+d%) as the basic data and time as the number axis, an overtaking speed curve is established. Extreme value search is performed on this overtaking speed data to determine whether to use a single-peak or multi-peak Gaussian function algorithm to fit the overtaking speed data curve in this planning cycle, thereby performing speed planning; where d% is the increase in the speed value of the overtaken vehicle, 0 <d%<100%;

[0018] Vehicle parking and driving status: The starting point of the planned speed curve is the end point of the driving speed curve planned in the previous cycle. The collected information includes the speed data at that point and the status of static obstacles near the vehicle. When the vehicle enters parking mode, the vehicle speed at the planning starting point is the maximum speed in the planned speed curve, and the end of the planned speed curve is when the speed is zero when parking. A minimum Gaussian function is set to plan the speed threshold, which is close to zero. Based on the vehicle speed at the planning starting point, the minimum Gaussian function planning speed threshold, the parking distance, and the total parking time, a single-peak Gaussian function is used to plan the speed curve. When the speed is lower than the minimum Gaussian function planning speed threshold, a slope speed planning method is used to reduce the vehicle speed to zero in a short time.

[0019] In the aforementioned vehicle following state, if the vehicle being followed is a vehicle starting from zero speed, a speed threshold is set, and the vehicle accelerates to the set speed threshold at a fixed acceleration along a slanted curve. Then, based on the speed data of the vehicle being followed collected in the previous cycle, a speed curve is planned using a single-peak, single-sided Gaussian function algorithm to achieve tracking of vehicles starting from zero speed.

[0020] The unimodal Gaussian function is:

[0021]

[0022] Among them, t i v is a point in time. i The velocity collected at this time point, where i represents the index of the data point collected in the previous period, i = 1, 2, 3, ..., v max The peak value of the Gaussian curve is t. m S represents the peak position, and S represents the half-width information.

[0023] The aforementioned multi-peak Gaussian function

[0024]

[0025] Among them, ti v is a point in time. i The velocity corresponds to the specified time point; i represents the index of the data point collected in the previous cycle, i = 1, 2, 3, ...; v jmax t represents the peak value of each peak of the Gaussian curve. jm S represents the peak position of each peak. j The half-width information for each peak is given, where j represents the index number of each peak in the velocity data collected in the previous cycle, j = 1, 2, 3, ...

[0026] The process of fitting the velocity curve using the Gaussian function algorithm based on unimodal and multimodal Gaussian functions is as follows:

[0027] The process of fitting a single-peak Gaussian function:

[0028] Taking the natural logarithm of both sides of equation (1), we get equation (3):

[0029]

[0030] make:

[0031]

[0032] Based on the data used, equation (3) can be expressed as follows from equation (4):

[0033]

[0034] Equation (5) can be written as:

[0035] Z = XB (6)

[0036] To minimize the variance of the fitted function curve across all data points, the optimal value of B is estimated using the least squares theorem. The generalized least squares solution of matrix B is:

[0037] B = (X) T X) -1 X T Z (7)

[0038] After obtaining b0, b1, and b2, the parameter v to be estimated can be obtained according to equation (4). max t m The values ​​of S are obtained, which are the characteristic parameters of the Gaussian function:

[0039]

[0040] The parameter v to be estimated can be obtained based on formula (8). max t m The values ​​of S are obtained, which are the characteristic parameters of the Gaussian function, and the target velocity planning curve is obtained.

[0041] Multi-peak Gaussian function fitting process:

[0042] Each peak in the multi-peak Gaussian function fitting curve is calculated according to the single-peak Gaussian function fitting process described above, and finally a target velocity planning curve containing multiple peaks is formed.

[0043] In step D, the target performance function is established as follows:

[0044]

[0045] Among them, the fuel consumption term represents the fuel consumption of the hybrid vehicle in the prediction time domain, while the energy consumption term is to ensure that the battery SOC is as close as possible to the expected SOC in the prediction time domain. r value;

[0046] In the formula, t is the current time, Δt is the unit sampling time, p is the prediction domain length, and the prediction time domain is ([t, t+pΔt]∈[t0, tf]). fuel and w soc All are weighting coefficients. For fuel consumption rate, SOC r (t) is the reference battery SOC trajectory value, and SOC(t) is the battery's state of charge at time t.

[0047] The function of the reference battery SOC trajectory value is:

[0048]

[0049]

[0050]

[0051] Where SOC0 is the initial value of SOC, and SOC f S is the final value of SOC. z Let S(t) be the total distance traveled by the vehicle at time t. p v(t) represents the vehicle's distance traveled within the prediction domain, and v(t) represents the vehicle's speed at time t. p (t) represents the vehicle speed in the predicted time domain;

[0052] The planned vehicle speed sequence in the prediction time domain is v(t+Δt), v(t+2Δt), ..., v(t+p(-1)Δt), v(t+pΔt).

[0053] The solution function for SOC(t) in the target performance function is:

[0054]

[0055] In the formula, SOC(t) represents the state of charge of the battery at time t, and Q... c V is the rated capacity of the battery. oc R0 is the battery open-circuit voltage, P is the battery internal resistance, and P is the open-circuit voltage. batt This refers to the battery's output power.

[0056] The constraints on the target performance function are as follows:

[0057] Vehicle tire power requirement P wheel formula:

[0058]

[0059] Among them, P wheel M is the power required by the vehicle tires; g is the vehicle's full load mass; f is the acceleration due to gravity; θ is the rolling resistance coefficient; v is the slope angle; C is the vehicle speed. D dv / dt is the drag coefficient; A is the vehicle's frontal area; dv / dt is the vehicle's acceleration.

[0060] The expression for the output power relationship between the engine and the engine-generator set is as follows:

[0061] P ft (t)=P e (t)·η g (13)

[0062] Among them, P ft P represents the output power of the engine-generator set. e η is the engine's output power. g The efficiency of the generator;

[0063] The engine operates on the economic operating curve, and the corresponding operating point is the economic operating point. The engine fuel consumption model is fitted using a polynomial equation:

[0064]

[0065] in, Engine fuel consumption rate; T e This refers to the engine's output torque; w e 'a' represents the engine output speed; 'a' represents the polynomial coefficients.

[0066] Without considering mechanical braking, the motor power expression is:

[0067]

[0068] Among them, P m T represents the motor power. m w is the output torque of the motor. mη is the motor output speed. m η′ represents the efficiency of the motor in electric mode. m η is the efficiency of the motor in the generator state. i T represents the overall efficiency of the speed reduction transmission system. m >0, the motor is in driving mode, T m <0 indicates the motor is in generator mode;

[0069] η m =f m (T m w m (16)

[0070] η′ m =f′ m (T m w m (17)

[0071] The instantaneous power consumed by a series hybrid vehicle is equal to the power provided by the energy source, which satisfies:

[0072] P m (t)+P el (t)=P ft (t)+P batt (t) (18)

[0073] P m In reality, it refers to the motor power; the motor's output power must meet the power requirements of the speed planning. ft It refers to the output power of the engine-generator set, P. batt It refers to the battery's power; P el (t) - Power consumed by other electrical loads;

[0074] The inequality constraints used are:

[0075]

[0076] Select engine output power P e As a control variable, the battery SOC value is used as a state variable, and the fuel consumption m is used as a control variable. f Motor power P m Motor output torque T m Battery power P batt As output, by solving the multi-objective, multi-constraint optimization problem in the prediction domain in real time, the optimal sequence of control variables is obtained. The first element is selected and applied to the vehicle, and the vehicle state is updated in the next moment to achieve rolling optimization control.

[0077] This invention also discloses a speed-planning energy optimization control system for hybrid vehicles, comprising:

[0078] Acquisition module: Used to collect speed data of this vehicle and related vehicles, as the basis data for the planned speed curve of this vehicle in this cycle;

[0079] Speed ​​planning module: This module is used to fit the speed curve to the collected speed data using a Gaussian function algorithm, perform speed planning, and obtain the target speed planning curve for this vehicle in this cycle. The Gaussian function algorithm can be either a single-peak Gaussian function or a multi-peak Gaussian function, depending on whether the collected speed data has a single or multiple extreme values.

[0080] Energy Management Module: This module incorporates a series hybrid electric vehicle (SHEV) model, including an engine-generator set, a drive motor, and a battery pack. It also establishes a target performance function based on two coordinated optimization objectives: reducing engine fuel consumption and maintaining stable battery state of charge (SOC) relative to the reference trajectory. Using a model predictive control algorithm based on the target speed planning curve, the module solves the target performance function, calculates the optimal control sequence for the entire driving process, and obtains the engine output power control curve for that process. Based on this engine output power control curve, the module controls the vehicle engine to operate on the economic curve, ensuring vehicle power demand while reducing fuel consumption. The vehicle's planned speed is fed into the model predictive controller. By solving a multi-objective, multi-constraint optimization problem, the optimal engine power control sequence is obtained in the prediction time domain. Online rolling optimization and feedback self-correction methods are then used to achieve optimal control throughout the entire driving process.

[0081] The hardware mainly includes: using GPS devices and vehicle speed sensors, radar sensors to monitor the distance to the vehicle in front and whether there are oncoming vehicles on both sides of the vehicle, and speed measuring devices to measure the speed of other vehicles, to obtain information such as the vehicle's historical travel conditions, current vehicle position, distance to the vehicle in front, and speed; memory is used to store programs and data; processor is used to calculate and run the programs, including vehicle speed planning and energy management control programs.

[0082] The method and system of this invention utilize a Gaussian function fitting algorithm to plan the speed curve of autonomous driving for hybrid vehicles. This speed planning method and system can reflect the overall changes in the data, effectively improving the comfort and accuracy of autonomous driving. Through model predictive control algorithm, the optimal control action is found in the predicted time domain, so as to optimize local energy consumption, achieve high computational efficiency, good real-time controllability, and good feasibility. Attached Figure Description

[0083] Figure 1 A schematic diagram of velocity fitting for a single-peaked Gaussian function;

[0084] Figure 2 A schematic diagram of velocity fitting for a multi-peak Gaussian function;

[0085] Figure 3 Schematic diagram of the powertrain system of a hybrid vehicle;

[0086] Figure 4 Workflow diagram Detailed Implementation

[0087] Example 1

[0088] 1. Classify driving conditions according to driving purpose and set the data collection content for different categories of speed planning curves;

[0089] 2. Determine which category the proposed speed curve belongs to, collect data, and use GPS devices, vehicle speed sensors, radar sensors that monitor the distance to the vehicle in front and whether there are oncoming vehicles on both sides of the vehicle, and speed measuring devices to measure the speed of other vehicles to obtain historical travel data, current vehicle position and distance to the vehicle in front, speed and other information, as the basic data for the planned speed curve of this vehicle in this cycle.

[0090] 3. Use the Gaussian function algorithm to fit the speed curve, perform speed planning, and obtain the target speed planning curve for this vehicle in this cycle to determine the vehicle's required power.

[0091] The unimodal Gaussian function is:

[0092]

[0093] Among them, t i v is a point in time. i The velocity collected at this time point, where i represents the index of the data point collected in the previous period, i = 1, 2, 3, ..., v max The peak value of the Gaussian curve is t. m S represents the peak position, and S represents the half-width information.

[0094] The aforementioned multi-peak Gaussian function

[0095]

[0096] Among them, t i v is a point in time. i The velocity corresponds to the specified time point; i represents the index of the data point collected in the previous cycle, i = 1, 2, 3, ...; v jmax t represents the peak value of each peak of the Gaussian curve. jm S represents the peak position of each peak. j The half-width information for each peak is given, where j represents the index number of each peak in the velocity data collected in the previous cycle, j = 1, 2, 3, ...

[0097] The process of fitting the velocity curve using the Gaussian function algorithm based on unimodal and multimodal Gaussian functions is as follows:

[0098] The process of fitting a single-peak Gaussian function:

[0099] Taking the natural logarithm of both sides of equation (1), we get equation (3):

[0100]

[0101] Command: ln v i =z i

[0102]

[0103]

[0104]

[0105] Based on the data used, equation (3) can be expressed as follows from equation (4):

[0106]

[0107] Equation (5) can be written as:

[0108] Z = XB (6)

[0109] To minimize the variance of the fitted function curve across all data points, the optimal value of B is estimated using the least squares theorem. The generalized least squares solution of matrix B is:

[0110] B(X T X) -1 X T Z (7)

[0111] After obtaining b0, b1, and b2, the parameter v to be estimated can be obtained according to equation (4). max t m The values ​​of S are obtained, which are the characteristic parameters of the Gaussian function:

[0112]

[0113] The parameter v to be estimated can be obtained based on formula (8). max t m The values ​​of S are obtained, which are the characteristic parameters of the Gaussian function, and the target velocity planning curve is obtained.

[0114] Multi-peak Gaussian function fitting process:

[0115] Each peak in the multi-peak Gaussian function fitting curve is calculated according to the single-peak Gaussian function fitting process described above, and finally a target velocity planning curve containing multiple peaks is formed.

[0116] 4. The established hybrid vehicle model is shown below. Figure 3 As shown, it includes an engine-generator set, a drive motor, and a battery pack;

[0117] 5. Establish the SOC state equation for the battery model.

[0118]

[0119] In the formula, SOC(t) represents the state of charge of the battery at time t, and Q... c V is the rated capacity of the battery. oc R0 is the battery open-circuit voltage, P is the battery internal resistance, and P is the open-circuit voltage. batt This refers to the battery's output power.

[0120] 6. The constraints for determining the objective performance function are as follows:

[0121] Vehicle tire power requirement P wheel formula:

[0122]

[0123] Among them, P wheel M is the power required by the vehicle tires; g is the vehicle's full load mass; f is the acceleration due to gravity; θ is the rolling resistance coefficient; v is the slope angle; C is the vehicle speed. D dv / dt is the drag coefficient; A is the vehicle's frontal area; dv / dt is the vehicle's acceleration.

[0124] The expression for the output power relationship between the engine and the engine-generator set is as follows:

[0125] P ft (t)=P e (t)·η g (13)

[0126] Among them, P ft P represents the output power of the engine-generator set. e η is the engine's output power. g The efficiency of the generator;

[0127] The engine operates on the economic operating curve, and the corresponding operating point is the economic operating point. The engine fuel consumption model is fitted using a polynomial equation:

[0128]

[0129] in, Engine fuel consumption rate; T e This refers to the engine's output torque; w e 'a' represents the engine output speed; 'a' represents the polynomial coefficients.

[0130] Without considering mechanical braking, the motor power expression is:

[0131]

[0132] Among them, P m T represents the motor power. m w is the output torque of the motor. m η is the motor output speed. m η′ represents the efficiency of the motor in electric mode. m η is the efficiency of the motor in the generator state. i T represents the overall efficiency of the speed reduction transmission system. m >0, the motor is in driving mode, T m <0 indicates the motor is in generator mode;

[0133] η m =f m (T m w m (16)

[0134] η′ m =f′ m (T m w m (17)

[0135] The instantaneous power consumed by a series hybrid vehicle is equal to the power provided by the energy source, which satisfies:

[0136] P m (t)+P el (t)=P ft (t)+P batt (t) (18)

[0137] P m In reality, it refers to the motor power; the motor's output power must meet the power requirements of the speed planning. ft It is the output power of the engine-generator set, P batt It is the battery's power; P el (t) represents the power consumed by other electrical loads.

[0138] The inequality constraints are:

[0139]

[0140] 7. The target performance function was determined, taking into account engine fuel consumption and battery SOC stability relative to the reference trajectory, and the two objectives were optimized in a coordinated manner. The target performance function is as follows:

[0141]

[0142] Among them, the fuel consumption term represents the fuel consumption of the hybrid vehicle in the prediction time domain, while the energy consumption term is to ensure that the battery SOC is as close as possible to the expected SOC in the prediction time domain. r value.

[0143] In the formula, t is the current time, Δt is the unit sampling time, p is the prediction domain length, and the prediction time domain is ([t, t+pΔt]∈[t0, tf]). fuel and w soc All are weighting coefficients. For fuel consumption rate, SOC r (t) is the reference battery SOC trajectory value.

[0144] The planned vehicle speed sequence in the prediction time domain is v(t+Δt), v(t+2Δt), ..., v(t+(p-1)Δt), v(t+pΔt).

[0145] 8. Calculation of Battery SOC Trajectory Value

[0146]

[0147]

[0148]

[0149] Where SOC0 is the initial value of SOC, and SOC f S is the final value of SOC. z Let S(t) be the total distance traveled by the vehicle at time t. p v(t) represents the vehicle's distance traveled within the prediction domain, and v(t) represents the vehicle's speed at time t. p (t) represents the vehicle speed in the predicted time domain.

[0150] 9. Using engine output power P e As a control variable, the battery SOC value is used as a state variable, and the fuel consumption m is used as a control variable. f Motor power P m (motor output torque T) m ), battery power, etc. batt As output, by solving the multi-objective, multi-constraint optimization problem in the prediction domain in real time, the optimal sequence of control variables is obtained. The first element is selected and applied to the vehicle. The vehicle state is updated in the next moment to achieve rolling optimization control.

[0151] Figure 4 This is a flowchart illustrating the energy optimization method described in this invention.

[0152] Currently, the energy optimization method for hybrid vehicles based on speed planning of this invention has been simulated and tested. The results show that the method of this invention has the following advantages: 1) It improves the comfort and accuracy of autonomous driving; 2) It can better utilize global optimization to find the local optimal control action in each predicted time domain, ensuring optimality and real-time control, achieving the expected control objectives, and making the system have good robustness and stability.

[0153] Example 2

[0154] This invention provides an apparatus system for implementing the method of Embodiment 1; comprising the following modules:

[0155] Acquisition module: Used to collect speed data of this vehicle and related vehicles, as the basis data for the planned speed curve of this vehicle in this cycle;

[0156] Speed ​​planning module: This module is used to fit the speed curve to the collected speed data using a Gaussian function algorithm, perform speed planning, and obtain the target speed planning curve for this vehicle in this cycle. The Gaussian function algorithm can be either a single-peak Gaussian function or a multi-peak Gaussian function, depending on whether the collected speed data has a single or multiple extreme values.

[0157] Energy Management Module: This module incorporates a series hybrid electric vehicle (SHEV) model, including an engine-generator set, a drive motor, and a battery pack. It also establishes a target performance function based on two coordinated optimization objectives: reducing engine fuel consumption and maintaining stable battery state of charge (SOC) relative to the reference trajectory. Using a model predictive control algorithm based on the target speed planning curve, the module solves the target performance function, calculates the optimal control sequence for the entire driving process, and obtains the engine output power control curve for that process. Based on this engine output power control curve, the module controls the vehicle engine to operate on the economic curve, ensuring vehicle power demand while reducing fuel consumption. The vehicle's planned speed is fed into the model predictive controller. By solving a multi-objective, multi-constraint optimization problem, the optimal engine power control sequence is obtained in the prediction time domain. Online rolling optimization and feedback self-correction methods are then used to achieve optimal control throughout the entire driving process.

[0158] The hardware mainly includes: using GPS devices and vehicle speed sensors, radar sensors to monitor the distance to the vehicle in front and whether there are oncoming vehicles on both sides of the vehicle, and speed measuring devices to measure the speed of other vehicles, to obtain information such as the vehicle's historical travel conditions, current vehicle position, distance to the vehicle in front, and speed; memory is used to store programs and data; processor is used to calculate and run the programs, including vehicle speed planning and energy management control programs.

Claims

1. A method for energy optimization control of hybrid vehicles based on speed planning, characterized in that... Includes the following steps: A. Classify driving conditions according to driving purpose and set the data collection content for different categories of speed planning curves; The driving status classification is specifically as follows: vehicle following other vehicles, vehicle overtaking other vehicles, and vehicle parking in a parking space. B. Determine which category the proposed speed curve belongs to, collect speed data as the basis for the planned speed curve of this vehicle in this cycle, use the Gaussian function algorithm to fit the speed curve, perform speed planning, and obtain the target speed planning curve of this vehicle in this cycle. Among them, the Gaussian function algorithm includes a single-peak Gaussian function and a multi-peak Gaussian function, which are selected according to whether the collected velocity data is a single-peak or multi-peak value; The process of planning the speed curve is as follows: Vehicle following status: The starting point of the planned speed curve is the end point of the planned speed curve in the previous cycle. The collected information includes the speed and acceleration data of the current vehicle at this point, as well as the speed and acceleration data of the vehicle being followed in the previous cycle. An extreme value search is performed on the speed data of the vehicle being followed in the previous cycle to determine whether to use a single-peak or multi-peak Gaussian function algorithm for speed planning in this planning cycle. Vehicle overtaking driving status: The starting point of the planned speed curve is the end point of the driving speed curve planned in the previous cycle. The collected information includes the vehicle's speed and acceleration data at this point, as well as the speed and acceleration data of vehicles in front, to the left, to the right, and behind the vehicle collected in the previous cycle; the vehicle in front is the overtaking target; based on the speed Vi of the overtaken vehicle collected in the previous cycle, with speed Vi*(1+d%) as the basic data and time as the number axis, an overtaking speed curve is established. Extreme value search is performed on this overtaking speed data to determine whether to use a single-peak or multi-peak Gaussian function algorithm to fit the overtaking speed data curve in this planning cycle, thereby performing speed planning; where d% is the increase in the speed value of the overtaken vehicle, 0 <d%<100%; Vehicle parking and driving status: The starting point of the planned speed curve is the end point of the driving speed curve planned in the previous cycle. The collected information includes the speed data at that point and the status of static obstacles near the vehicle. When the vehicle enters parking mode, the vehicle speed at the planning starting point is the maximum speed in the planned speed curve, and the end of the planned speed curve is when the speed is zero when parking. A minimum Gaussian function is set to plan the speed threshold, which is close to zero. Based on the vehicle speed at the planning starting point, the minimum Gaussian function planning speed threshold, the parking distance, and the total parking time, a single-peak Gaussian function is used for speed curve planning. When the speed is lower than the minimum Gaussian function planning speed threshold, a slope speed planning method is used to reduce the vehicle speed to zero in a short time. C. Establish a series hybrid electric vehicle (SHEV) model, including an engine-generator set, a drive motor, and a battery pack; D. Based on the two coordinated optimization objectives of reducing engine fuel consumption and stabilizing battery SOC relative to the reference trajectory, establish the objective performance function; E. Based on the target speed planning curve, the model predictive control algorithm is used to solve the target performance function and calculate the optimal control sequence for the entire driving process of the target speed planning curve, thereby obtaining the engine output power control curve for this driving process; based on the engine output power control curve, the vehicle engine is controlled to operate on the economic curve, ensuring the vehicle's power demand while reducing vehicle fuel consumption.

2. The energy optimization control method for hybrid vehicles based on speed planning as described in claim 1, characterized in that: In the aforementioned vehicle following state, if the vehicle being followed is a vehicle starting from zero speed, a speed threshold is set, and the vehicle accelerates to the set speed threshold at a fixed acceleration along a slanted curve. Then, based on the speed data of the vehicle being followed collected in the previous cycle, a speed curve is planned using a single-peak, single-sided Gaussian function algorithm to achieve tracking of vehicles starting from zero speed.

3. The energy optimization control method for hybrid vehicles based on speed planning as described in claim 1, characterized in that: The unimodal Gaussian function is: Where ti is the time point, vi is the speed collected at that time point, and i represents the index number of the data point collected in the previous period, i = 1, 2, 3, ..., v max The peak value of the Gaussian curve is t. m S represents the peak position, and S represents the half-width information. The aforementioned multi-peak Gaussian function Among them, t i v is a point in time. i The velocity corresponds to the specified time point; i represents the index of the data point collected in the previous cycle, i = 1, 2, 3, ...; v jmax t represents the peak value of each peak of the Gaussian curve. jm S represents the peak position of each peak. j The half-width information for each peak is given, where j represents the index number of each peak in the velocity data collected in the previous cycle, j = 1, 2, 3, ...

4. The energy optimization control method for hybrid vehicles based on speed planning as described in claim 3, characterized in that: The process of fitting the velocity curve using the Gaussian function algorithm based on unimodal and multimodal Gaussian functions is as follows: The process of fitting a single-peak Gaussian function: Taking the natural logarithm of both sides of equation (1), we get equation (3): make: Based on the data used, equation (3) can be expressed as follows from equation (4): Equation (5) can be written as: Z = XB (6) To minimize the variance of the fitted function curve across all data points, the optimal value of B is estimated using the least squares theorem. The generalized least squares solution of matrix B is: B=(X T X) -1X TZ (7) After obtaining b0, b1, and b2, the parameter v to be estimated can be obtained according to equation (4). max t m、 The value of S is obtained, which gives the characteristic parameters of the Gaussian function: The parameter v to be estimated can be obtained based on formula (8). max t m The values ​​of S are obtained, which are the characteristic parameters of the Gaussian function, and the target velocity planning curve is obtained. Multi-peak Gaussian function fitting process: Each peak in the multi-peak Gaussian function fitting curve is calculated according to the single-peak Gaussian function fitting process described above, and finally a target velocity planning curve containing multiple peaks is formed.

5. The energy optimization control method for hybrid vehicles based on speed planning as described in claim 1, characterized in that: In step D, the target performance function is established as follows: Among them, the fuel consumption term represents the fuel consumption of the hybrid vehicle in the prediction time domain, while the energy consumption term is to ensure that the battery SOC is as close as possible to the expected SOC in the prediction time domain. r value; In the formula, t is the current time, Δt is the unit sampling time, p is the prediction domain length, and the prediction time domain is ([t, t+pΔt]∈[t0, tf]). fuel and w soc All are weighting coefficients. For fuel consumption rate, SOC r (t) is the reference battery SOC trajectory value, and SOC(t) is the battery's state of charge at time t.

6. The energy optimization control method for hybrid vehicles based on speed planning as described in claim 5, characterized in that: The function of the reference battery SOC trajectory value is: Where SOC0 is the initial value of SOC, SO C f is the final value of SOC, S z Let S(t) be the total distance traveled by the vehicle, S(t) be the distance traveled by the vehicle at time t, Sp(t) be the distance traveled by the vehicle within the prediction domain, and v(t) be the speed of the vehicle at time t. p (t) represents the vehicle speed in the predicted time domain; The planned vehicle speed sequence in the prediction time domain is v(t+Δt), v(t+2Δt), ..., v(t+(p-1)Δt), v(t+pΔt); The solution function for SOC(t) in the target performance function is: In the formula, SOC(t) represents the state of charge of the battery at time t, and Q... c V is the rated capacity of the battery. oc R0 is the battery open-circuit voltage, P is the battery internal resistance, and P is the open-circuit voltage. batt This refers to the battery's output power.

7. The energy optimization control method for hybrid vehicles based on speed planning as described in claim 6, characterized in that: The constraints on the target performance function are as follows: Vehicle tire power requirement P wheel formula: Among them, P wheel M is the power required by the vehicle tires; g is the vehicle's full load mass; f is the acceleration due to gravity; θ is the rolling resistance coefficient; v is the slope angle; C is the vehicle speed. D dv / dt is the drag coefficient; A is the vehicle's frontal area; dv / dt is the vehicle's acceleration. The expression for the output power relationship between the engine and the engine-generator set is as follows: P ft (t)=P e (t)·η g (13) Among them, P ft P represents the output power of the engine-generator set. e η is the engine's output power. g The efficiency of the generator; The engine operates on the economic operating curve, and the corresponding operating point is the economic operating point. The engine fuel consumption model is fitted using a polynomial equation: in, Engine fuel consumption rate; T e This refers to the engine's output torque; w e 'a' represents the engine output speed; 'a' represents the polynomial coefficients. Without considering mechanical braking, the motor power expression is: Among them, P m T represents the motor power. m w is the output torque of the motor. m η is the motor output speed. m The efficiency of the motor in electric mode; η′ m η is the efficiency of the motor in the generator state. i T represents the overall efficiency of the speed reduction transmission system. m >0, the motor is in driving mode, T m <0 indicates the motor is in generator mode; or m =f m (T m ,w m ) (16) or' m =f′ m (T m ,w m ) (17) In a series hybrid vehicle, the instantaneous power consumed is equal to the power supplied by the energy source, which satisfies: P m (t)+P el (t)=P ft (t)+P batt (t) (18) P m In reality, it refers to the motor power; the motor's output power must meet the power requirements of the speed planning. ft It refers to the output power of the engine-generator set, P. batt It refers to the battery's power; P el (t) represents the power consumed by other electrical loads; The inequality constraints used are: Select engine output power P e As a control variable, the battery SOC value is used as a state variable, and the fuel consumption m is used as a control variable. f Motor power P m Motor output torque T m Battery power P batt As output, by solving the multi-objective, multi-constraint optimization problem in the prediction domain in real time, the optimal sequence of control variables is obtained. The first element is selected and applied to the vehicle, and the vehicle state is updated in the next moment to achieve rolling optimization control.

8. A hybrid vehicle energy optimization control system based on speed planning, characterized in that, include: Acquisition module: Used to collect speed data of this vehicle and related vehicles, as the basis data for the planned speed curve of this vehicle in this cycle; Speed ​​planning module: This module is used to fit the speed curve to the collected speed data using a Gaussian function algorithm, perform speed planning, and obtain the target speed planning curve for this vehicle in this cycle. The Gaussian function algorithm can be either a single-peak Gaussian function or a multi-peak Gaussian function, depending on whether the collected speed data has a single or multiple extreme values. Energy Management Module: This module incorporates a series hybrid electric vehicle (SHEV) model, including an engine-generator set, a drive motor, and a battery pack. It also establishes a target performance function based on two coordinated optimization objectives: reducing engine fuel consumption and maintaining stable battery state of charge (SOC) relative to the reference trajectory. Using a model predictive control algorithm based on the target speed planning curve, the module solves the target performance function, calculates the optimal control sequence for the entire driving process, and obtains the engine output power control curve for that process. Based on this engine output power control curve, the module controls the vehicle engine to operate on the economic curve, ensuring vehicle power demand while reducing fuel consumption. The vehicle's planned speed is fed into the model predictive controller. By solving a multi-objective, multi-constraint optimization problem, the optimal engine power control sequence is obtained in the prediction time domain. Online rolling optimization and feedback self-correction methods are then used to achieve optimal control throughout the entire driving process.

Images