CN105083276B - Hybrid vehicle energy-conservation forecast Control Algorithm based on decentralised control - Google Patents

Abstract

The invention discloses a hybrid electric vehicle energy-saving predictive control method based on decentralized control, comprising the following steps: obtaining real-time traffic of the own vehicle, the vehicle in front and the vehicle behind from the global positioning system, the inter-vehicle communication system, the vehicle-road communication system and the intelligent transportation system Information is used as system input; a mathematical model of hybrid vehicle platooning decentralized control is established as the basis for predicting the future vehicle state; the optimal control problem of hybrid vehicle platooning control is defined, and the function equation for solving the optimal control quantity is provided; real-time feedback is optimal Control, solve the optimal control amount, use the planetary gear mechanism as the electronic continuously variable transmission, the engine works at the best working point, use the road traffic information to predict the driving status of the vehicle in front and behind, and adjust the energy flow of the hybrid vehicle online to achieve energy saving and reduction. platoon objectives, greatly reducing the computation time and improving the real-time control characteristics of the vehicle.

Description

Energy saving predictive control method for hybrid electric vehicles based on distributed control

technical field

The invention relates to a distributed control-based energy-saving predictive control method for a hybrid electric vehicle, in particular to a real-time optimal control method for a hybrid electric vehicle.

Background technique

The increasingly severe global energy and environmental situation, especially the rapid growth of car ownership, promotes the development of new energy vehicles and intelligent transportation systems. In order to solve the three major problems of traffic congestion, environmental deterioration and traffic accidents, the present invention proposes an energy-saving predictive control method for hybrid electric vehicles based on queue driving. The vehicle platoon driving technology refers to the technology in which multiple vehicles travel in a platoon with a small inter-vehicle distance. This technology can greatly improve the aerodynamic characteristics around the vehicle, reduce its air resistance, enhance traffic safety, and effectively improve the fuel economy of the vehicle. On the other hand, compared with traditional vehicles, hybrid vehicles have the redundancy of battery and fuel dual system drive. Using this redundancy can adjust the operating point of the driving device to the optimal position, so as to achieve the goal of energy saving and emission reduction. It is expected that the mainstream of future cars will be such hybrid cars. Since hybrid electric vehicles can recover the regenerative braking energy generated with vehicle deceleration and optimize the operating point of the drive device by using the redundancy of the drive system (engine and motor), it can greatly play the role of energy saving and emission reduction. However, the optimal operating point changes all the time with the characteristics of the engine, the driving state of the surrounding vehicles, and the change of road traffic conditions. Moreover, the rotating system (engine and electric motor) has speed and torque limits, and the battery has state-of-charge limits, and exceeding these limits has a great impact on the performance of key vehicle components. Therefore, the energy saving and emission reduction effect of HEV largely depends on its energy management strategy (satisfying constraints). And its key technology is the real-time optimization in the energy management central controller, in order to realize the commercialization and industrialization of the control strategy.

The control strategy of HEMS is the technical core and design difficulty of its research and development. The control strategies that have been proposed so far can be roughly divided into four categories: numerical optimal control, analytical optimal control, instantaneous optimal control and heuristic control. Typical representatives of numerical optimal control are dynamic programming and model predictive control. A typical representative of analytical optimal control is the Pontryagin minimum principle control strategy. The typical representative of instantaneous optimal control is the minimum instantaneous equivalent fuel consumption control strategy. The typical representative of the heuristic control strategy is the rule-based control strategy. The traditional global optimal control algorithm dynamic programming and the Pontryagin minimum principle control method cannot achieve real-time optimization because they need to know all future working conditions in advance. Traditional rule-based control strategies cannot maximize efficiency. The general feed-forward control (assuming that the vehicle speed mode is constant) cannot achieve real-time optimization. The traditional instantaneous optimal control parameters are too affected by changes in future vehicle operating conditions and cannot meet the control performance. At the same time, the platoon driving of hybrid electric vehicles can also greatly improve the fuel economy of the vehicles. Traditional hybrid vehicle platooning adopts a centralized control method, and the central controller controls all vehicles. This control method has disadvantages such as difficult operation and large amount of calculation. And the distributed control strategy that the present invention proposes owing to only controls self-vehicle, utilizes vehicle-to-vehicle communication technology, vehicle-road communication technology, global positioning system and intelligent transportation system to measure information such as the position, speed and acceleration of the front vehicle and the rear vehicle, greatly reducing The difficulty of operation and the amount of calculation have improved the real-time control performance.

Since the early 1990s, countries around the world have paid great attention to the research and development of hybrid electric vehicles and intelligent transportation systems, and have achieved some significant results and progress. In 1997, at the intelligent transportation system exhibition sponsored by the US Department of Transportation, the platoon driving technology composed of 8 vehicles was demonstrated. Japan's Toyota Motor Corporation realized the mass production of hybrid electric vehicles in 1997, and realized the mass production of plug-in hybrid electric vehicles in 2012. In 2009, US President Barack Obama announced plans for the next generation of advanced batteries and plug-in hybrid vehicles. In China, the national "Eleventh Five-Year" 863 plan has set up major projects for energy saving and new energy vehicles. During the doctoral study at Kyushu University, Japan, the applicant has mastered the model predictive control algorithm commonly used by Japanese companies and universities and the C/GMRES fast solution method proposed by Japanese scholar Toshiyuki Otsuka. The combination of these two methods solves the practical application problem of the advanced algorithm of model predictive control.

In this context, improving energy utilization efficiency, reducing automobile pollution to the environment and enhancing traffic safety have become the primary tasks for the development of today's automobile industry. At the same time, using road traffic information to further improve the efficiency of driving devices has become a realistic need for the development of today's society. In order to solve the above problems, it is necessary to design an industrialized hybrid electric vehicle model predictive control method based on decentralized control, so as to achieve the goal of energy saving and emission reduction.

Contents of the invention

In view of the above problems, the object of the present invention is to provide a hybrid electric vehicle model prediction method based on decentralized control that can predict future vehicle operating conditions in real time, so as to achieve maximum energy saving and emission reduction, and industrialize hybrid electric vehicle energy management center controller.

To achieve the above object, the present invention takes the following technical solutions:

A method for energy-saving predictive control of hybrid electric vehicles based on decentralized control, characterized in that: comprising the following steps:

Step 1) Information collection: the position information of the front vehicle, the rear vehicle and the own vehicle is collected by the global positioning system as real-time vehicle status feedback, and the speed of the front vehicle and the rear vehicle are collected by the on-board radar speed measuring device for tracking control. The traffic system, the vehicle-road communication system and the vehicle-to-vehicle communication system collect traffic signal information, real-time road condition information, and the speed and acceleration information of the own vehicle, the vehicle behind and the vehicle in front, which are used for intelligent traffic control. The Kalman filter utilizes the collected battery information to Determination of battery state of charge;

Step 2) Vehicle modeling: The planetary gear type hybrid electric vehicle contains five dynamic components, which are the engine, battery, two electric generators and wheels. It also has the function of electronic continuously variable transmission. According to the vehicle mechanical coupling and electronic coupling relationship, the system dynamic equation is written, and the dynamic equation is decoupled to obtain the state space model of the system, as shown in formula (1):

x＝[p _p v _p p ₂ v ₂ SOC ₂ p _f v _f ]

u＝[u ₂ P _batt2 ]

In the formula, x is the state quantity, u is the control quantity, the parameters p _p , v _p , p _f and v _f are the front vehicle position, the front vehicle speed, the rear vehicle position and the rear vehicle speed, and the parameters p ₂ , v ₂ and SOC ₂ is the position, speed and state of charge of the battery, the parameters u ₂ and P _batt2 are the driving acceleration of the vehicle and the charging and discharging power of the battery of the vehicle, and the parameters ρ, C _D2 , A ₂ , m ₂ , g, μ and θ ₂ are air density, air resistance coefficient of self-vehicle, windward area of self-vehicle, mass of self-vehicle, acceleration of gravity, rolling resistance coefficient and road gradient of self-vehicle, V _OC , R _batt and Q _batt are battery open circuit voltage, internal resistance and Capacity, due to the inertia of the vehicle in the prediction interval, it is assumed that the acceleration of the vehicle ahead is constant. If the speed of the vehicle ahead is greater than the maximum value or less than a certain value, the acceleration of the vehicle ahead is 0. If the traffic light is red in front, the speed of the vehicle is assumed to be 0. The vehicle in front stops at the position of the traffic signal light, and the speed mode of the vehicle's start and stop adopts the experimental curve and is measured by the characteristics of the actual driver;

The fuel economy evaluation of the vehicle adopts the Weilan linear model, as shown in formula (2):

In the formula, m _f is the fuel consumption rate, the parameter P _req is the required power of the vehicle, and c _f is a constant parameter;

Step 3) Formulate control strategy: The steps of predicting the optimal control strategy based on the energy management model of hybrid electric vehicles based on decentralized control are as follows: first, detect the state of the own vehicle, the rear vehicle and the front vehicle, including position, speed and acceleration information, and then use the established The mathematical model and formulaic control strategy are used to solve the optimal control problem, and finally the first control quantity of the optimal control sequence is applied to the system. Since the model predictive control is an interval optimal control, the optimal control quantity obtained by it is is the sequence whose quantity is the prediction interval divided by the sampling interval. The first control quantity of the optimal control sequence is the closest to the actual state, so it is used as the actual control quantity;

The definition of the optimal control problem is shown in formula (3):

subject to P _{batt2min ≤P batt2} (τ|t) _{≤P batt2max} (3)

u _2min ≤u ₂ (τ|t)≤u _2max

In the formula, T is the prediction interval, and the parameters P _batt2min , P _batt2max , u _2max and u _2min are control quantity constraints;

The definition of the evaluation function is shown in formula (4):

L＝w _x L _x +w _y L _y +w _z L _z +w _d L _d +w _e L _e +w _f L _f +w _r L _r +w _s L _s

L _y =(v ₂ -v _d ) ² /2

L _d ＝(SOC ₂ −SOC _d ) ² (4)

L _e ＝(m ₂ *w ₂ *v ₂ /1000-P _batt2 ) ² /2

L _f ＝(-ln[SOC ₂ -0.6]-ln[0.8-SOC ₂ ])

L _r ＝-ln(d _f -d _d )-ln(d _p -d _d )

L _s ＝(a _p ) ² /2+(a _f ) ² /2

In the formula, SOC _d is the state of charge of the target battery, v _d is the target speed of the vehicle, and its value is the optimal constant speed fuel economy speed of the vehicle, w _x , w _y , w _z , w _d , we _e , w _f , w _r and w _s are weight coefficients, d _d is the minimum vehicle distance, the evaluation function is set to float above the minimum vehicle distance, so as to increase the control degree of freedom and improve vehicle fuel economy, and the barrier function is used to deal with system state constraints, d _f is the distance between the rear vehicle and its own vehicle, d _p is the distance between the front vehicle and its own vehicle;

Step 4) Online optimal control: In order to ensure the real-time optimal performance of the system, the numerical fast solution method based on the Hamiltonian equation is used to solve the above optimal control problem, and the minimum value principle is used to convert the optimal control problem into a two-point edge When dealing with the differential equations and algebraic equations related to the Hamiltonian function, the partial space method is used to solve the value problem, which is a GMRES solution method;

At each sampling moment, first measure the front vehicle position, self-vehicle position, rear vehicle position, front vehicle speed, self-vehicle speed, rear vehicle speed, front-vehicle acceleration, self-vehicle acceleration, rear-vehicle acceleration, and self-vehicle battery charge Status real-time status signal, secondly use global positioning system, vehicle-to-vehicle communication system, vehicle-road communication system and intelligent transportation system to predict the status of vehicles and surrounding environment in a certain range in the future, including the acceleration of the front vehicle, the acceleration of the rear vehicle, the distance between the front vehicle and the vehicle, The distance between the following vehicle and the self-vehicle is again based on the established vehicle model and the optimal control problem, using the above numerical fast solution method to solve the optimal control sequence in the prediction interval, and applying the first control quantity of the optimal control sequence in the prediction interval to Then, at the next sampling time, the vehicle advances the prediction interval one step forward, and this cycle repeats to achieve online optimal control.

The present invention has the following advantages due to the adoption of the above technical scheme:

1) This control strategy can comprehensively utilize the information of the vehicle in front, the vehicle behind, and the vehicle behind, as well as the information of traffic lights, to simultaneously optimize the speed mode and charge-discharge mode of the hybrid electric vehicle, which is different from the traditional method that only optimizes the charge-discharge mode Condition.

2) The control strategy considers the information of traffic lights. In the case of stopping and restarting, the actual measured speed mode of parking and starting the vehicle is used to make the proposed control strategy closer to the actual situation, which is different from the traditional method that only considers Control while the vehicle is cruising.

3) A decentralized control model of hybrid electric vehicles based on platooning is proposed, which provides a general method guidance for the modeling of platooning of hybrid electric vehicles.

By using the method, the fuel economy, emission performance and safety performance of the hybrid electric vehicle can be greatly improved.

Description of drawings

Fig. 1 is a schematic structural diagram of a drive system of a planetary gear hybrid hybrid vehicle according to the present invention.

Fig. 2 is a flowchart of a method for energy-saving predictive control of a hybrid electric vehicle based on decentralized control.

Figure 3 is a structural diagram of a hybrid electric vehicle energy-saving predictive controller based on decentralized control.

In Fig. 1: 1. Engine; 2. Power splitter; 3. Generator; 4. Storage battery; 5. Inverter; 6. Electric motor; 7. Final reducer.

detailed description

The specific embodiments of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings.

The invention discloses a hybrid electric vehicle energy-saving predictive control method based on decentralized control, comprising the following steps: obtaining real-time traffic of the own vehicle, the vehicle in front and the vehicle behind from the global positioning system, the inter-vehicle communication system, the vehicle-road communication system and the intelligent transportation system Information is used as system input; a mathematical model of hybrid vehicle platooning decentralized control is established as the basis for predicting the future vehicle state; the optimal control problem of hybrid vehicle platooning control is defined, and the function equation for solving the optimal control quantity is provided; real-time feedback is optimal control, to find the optimal control quantity. In the case of satisfying the safety distance between vehicles, the present invention adopts a hybrid electric vehicle energy-saving predictive control method based on decentralized control, according to the information obtained from the global positioning system, radar, intelligent transportation system, vehicle-road communication system and vehicle-to-vehicle communication system On-line adjustment optimizes the energy flow of the hybrid electric vehicle, and then the optimal performance of the hybrid electric vehicle system can be obtained. This method uses the planetary gear mechanism as an electronic continuously variable transmission to make the engine always work at its optimum working point. At the same time, it uses road traffic information to predict the driving status of the vehicle in front and behind, and adjusts the energy flow of hybrid electric vehicles online to achieve the goal of energy saving and emission reduction. In addition, the present invention is different from the traditional centralized control method, which greatly reduces the calculation time, improves the real-time control characteristics of the vehicle, and provides a new way to improve the performance of the central controller of the energy management system of the hybrid electric vehicle.

Fig. 1 is a structural diagram of the research object of the control method of the present invention. Use this block diagram during vehicle modeling to analyze system mechanical and electrical coupling relationships. The structure diagram contains five dynamic components of the hybrid vehicle. They are the engine, battery, 2 electric generators and wheels. The electric motor is connected to the wheels through the final reducer to transmit the power of the system. As a power distribution device, the planetary gear not only functions as a speed coupling, but also as an electronic continuously variable transmission. The planetary gear is mechanically coupled to the engine and 2 electric generators. The inverter is electrically coupled to the storage battery and 2 electric generators. An independent 3-DOF system model is obtained by decoupling the mechanical coupling and electrical coupling of the system. The control method of the present invention is system software, and Fig. 1 shows system hardware.

Figure 2 reveals the process of the whole control method. The collected information is used as input to the system model. The speed of the front vehicle and the speed of the rear vehicle are collected by the on-board radar speed measuring device for tracking control. Traffic signal information and real-time road condition information are collected by the intelligent transportation system, vehicle-to-vehicle communication system, and vehicle-road communication system for intelligent traffic control. The battery state of charge is estimated by the Kalman filter using the collected battery information. Vehicle modeling provides the models needed to predict future vehicle states for formulating model predictive control strategies. The formulated control strategy provides the functional equations to be solved for online optimal control.

Fig. 3 is a specific control structure diagram of the present invention. The location of the vehicle, the position of the front vehicle, the position of the rear vehicle, the speed of the vehicle, the speed of the front vehicle, the speed of the rear vehicle, and the acceleration of the vehicle are collected by the global positioning system, vehicle-mounted radar device, intelligent transportation system, vehicle-to-vehicle communication system, and vehicle-road communication system. Front vehicle acceleration and rear vehicle acceleration. The road gradient of the vehicle location is obtained by the global positioning system through the vehicle location query. Generates a target battery state of charge based on road grade. The measured vehicle state, road slope information, the position and speed of the front and rear vehicles, and traffic information, the target battery charge state, and the target vehicle speed are input into the model predictive controller, and the model predictive controller solves the optimal control problem according to the vehicle system model , get the optimal control amount, and act on the vehicle.

Embodiment: Taking the planetary gear type hybrid hybrid drive system as an example for illustration, as shown in Figure 1, the present invention discloses a hybrid electric vehicle energy-saving predictive control method based on decentralized control, the first step is information collection, the second The second step is the vehicle modeling, the third step is the formulation of the control strategy, and the fourth step is the online optimal control. The principle of the method is shown in Figure 2, and the specific control method includes the following steps:

Step 1) Information collection:

The global positioning system collects the position information of the front vehicle, the rear vehicle and the own vehicle as real-time vehicle status feedback, and the vehicle-mounted radar speed measuring device collects the speed of the front vehicle and the speed of the rear vehicle for tracking control. Intelligent transportation system, vehicle-road communication The system and the vehicle-to-vehicle communication system collect traffic signal information, real-time road condition information, and the speed and acceleration information of the own vehicle, the rear vehicle and the front vehicle for intelligent traffic control; the Kalman filter uses the collected battery information to determine the state of charge of the battery. Make an estimate.

Step 2) Vehicle modeling: The planetary gear type hybrid electric vehicle contains five dynamic components, which are engine 1, battery 4, generator 3, motor 6 and wheels. As a power distribution device, the power splitter 2 not only functions as a speed coupling, but also as an electronic continuously variable transmission. According to the vehicle mechanical coupling and electronic coupling relationship, the system dynamic equation can be written. By decoupling the dynamic equations, the state space model of the system can be finally obtained, as shown in formula (1):

x＝[p _p v _p p ₂ v ₂ SOC ₂ p _f v _f ]

u＝[u ₂ P _batt2 ]

In the formula, x is the state quantity, and u is the control quantity. The parameters p _p , v _p , p _f and v _f are the position of the front vehicle, the speed of the front vehicle, the position of the rear vehicle and the speed of the rear vehicle. The parameters p ₂ , v ₂ and SOC ₂ are the position, speed and state of charge of the battery 4 of the own vehicle. The parameters u ₂ and P _batt2 are the driving acceleration of the own vehicle and the charging and discharging power of the battery 4 of the own vehicle. The parameters ρ, C _D2 , A ₂ , m ₂ , g, μ and θ ₂ are air density, ego vehicle air resistance coefficient, ego vehicle frontal area, ego vehicle mass, gravitational acceleration, rolling resistance coefficient and ego vehicle road gradient. V _oc , R _batt and Q _batt are the open circuit voltage, internal resistance and capacity of the battery 4 . Due to the inertia of the vehicle in the prediction interval, it is assumed that the acceleration of the vehicle in front is constant. If the speed of the front vehicle is greater than the maximum value or less than a certain value, the acceleration of the front vehicle is 0. If a red traffic light is encountered ahead, it is assumed that a front vehicle with a speed of 0 stops at the position of the traffic light. The starting and stopping speed mode of the vehicle adopts the experimental curve and is measured by the characteristics of the actual driver.

The fuel economy evaluation of the vehicle adopts the Weilan linear model, as shown in formula (2):

Where m _f is the fuel consumption rate. The parameter P _req is the required power of the vehicle. c _f is a constant parameter.

Step 3) Formulate the control strategy:

The steps of predicting the optimal control strategy based on the distributed control hybrid electric vehicle energy management model are as follows: firstly detect the state of the own vehicle, the rear vehicle and the front vehicle, including position, speed and acceleration information, and then use the established mathematical model and formulaic control strategy Solve the optimal control problem, and finally apply the first control quantity of the obtained optimal control sequence to the system. Since the model predictive control is an interval optimal control, the optimal control quantity obtained by it is a sequence whose quantity is the prediction interval divided by the sampling interval. The first control quantity of the optimal control sequence is the closest to the actual state, so it is generally used as the actual control quantity.

The basic principle of model predictive control is: at each sampling moment, predict the future cost function of the system according to the prediction model, optimize the performance index in the future prediction interval, and perform feedback correction according to the output of the measured object, and the control strategy The design is transformed into an optimization process. The control sequence is obtained by solving the optimization problem of the corresponding prediction interval, and the first control quantity of the sequence is applied to the system to realize feedback control. Then, at the next sampling time, the prediction interval is pushed forward one step, continuously Repeat the process. In summary, it includes three parts: predictive model, rolling optimization and feedback control. The real-time optimal control of the system can be realized by predicting the future system input.

There are two characteristics of this control strategy. First, this control strategy can comprehensively utilize the information of the vehicle in front, the vehicle behind, and the vehicle behind, as well as the information of traffic lights, to optimize the speed mode and charging and discharging mode of the hybrid electric vehicle. Second, the control strategy considers the information of traffic lights, and uses the actual measured speed patterns of stopping and starting the vehicle when it needs to stop and restart, so that the proposed control strategy is closer to the actual situation. The above two characteristics are reflected in the evaluation function in the design of the control strategy, which provides a greater possibility for the performance improvement of the hybrid electric vehicle system.

Predictive models have been discussed in the previous section.

The definition of the optimal control problem is shown in formula (3):

subject to P _{batt2min ≤P batt2} (τ|t) _{≤P batt2max} (3)

u _2min ≤u ₂ (τ|t)≤u _2max

where T is the prediction interval. The parameters P _batt2min , P _batt2max , u _2max and u _2min are control quantity constraints.

The definition of the evaluation function is shown in formula (4):

L＝w _x L _x +w _y L _y +w _z L _z +w _d L _d +w _e L _e +w _f L _f +w _r L _r +w _s L _s

L _y =(v ₂ -v _d ) ² /2

L _d ＝(SOC ₂ −SOC _d ) ² (4)

L _e ＝(m ₂ *w ₂ *v ₂ /1000-P _batt2 ) ² /2

L _f ＝(-ln[SOC ₂ -0.6]-ln[0.8-SOC ₂ ])

L _r ＝-ln(d _f -d _d )-ln(d _p -d _d )

L _s ＝(a _p ) ² /2+(a _f ) ² /2

In the formula, SOC _d is the state of charge of the target battery 4 . v _d is the target speed of the vehicle, and its value is the optimal constant speed fuel economy speed of the vehicle. w _x , w _y , w _z , w _d , w _e , w _f , w _r and w _s are weight coefficients. d _d is the minimum distance between vehicles, and the evaluation function is set to make it float above the minimum distance between vehicles, so as to increase the degree of freedom of control and improve the fuel economy of vehicles. Barrier functions are used to deal with system state constraints, etc. d _f is the distance between the rear vehicle and its own vehicle, and d _p is the distance between the front vehicle and its own vehicle.

Step 4) Online optimal control:

In order to ensure the real-time optimal performance of the system, a fast numerical solution method based on the Hamiltonian equation is used to solve the above optimal control problems. Since it only needs a limited number of iterations to calculate the optimal solution of the numerical equation, the online performance of this method is very good. And because it is based on the Hamiltonian equation, the stability of this solution can be guaranteed. Solution Specifically, the optimal control problem is transformed into a two-point boundary value problem using the minimum value principle, and the partial space method is used to solve the differential equations and algebraic equations related to the Hamiltonian function. This is a GMRES solution. .

At each sampling moment, firstly, measure the front vehicle position, self-vehicle position, rear vehicle position, front vehicle speed, self-vehicle speed, rear vehicle speed, front-vehicle acceleration, self-vehicle acceleration, rear-vehicle acceleration and self-vehicle battery charge Second, use the global positioning system, vehicle-to-vehicle communication system, vehicle-road communication system and intelligent transportation system to predict the state of vehicles and the surrounding environment in a certain range in the future, including the acceleration of the front vehicle, the acceleration of the rear vehicle, and the relationship between the vehicle in front and the vehicle. Inter-vehicle distance, the distance between the following vehicle and the self-vehicle, etc. Thirdly, according to the established vehicle model and the optimal control problem, the optimal control sequence in the prediction interval is solved by using the above numerical fast solution method. Apply the first control quantity of the optimal control sequence within the prediction interval to the vehicle. Then at the next sampling time, the prediction interval is pushed forward one step, and this cycle repeats to realize online optimal control.

The present invention is also applicable to drive systems of other forms of hybrid electric vehicles, and the specific modeling method and control process are consistent with the drive systems of planetary gear hybrid hybrid electric vehicles, which will not be repeated here.

Claims (1)

1. a hybrid electric vehicle energy-saving predictive control method based on distributed control, is characterized in that: comprise the following steps: Step 1) Information collection: the position information of the front vehicle, the rear vehicle and the own vehicle is collected by the global positioning system as real-time vehicle status feedback, and the speed of the front vehicle and the rear vehicle are collected by the on-board radar speed measuring device for tracking control. The traffic system, the vehicle-road communication system and the vehicle-to-vehicle communication system collect traffic signal information, real-time road condition information, and the speed and acceleration information of the vehicle, the vehicle behind and the vehicle in front for intelligent traffic control, and the Kalman filter utilizes the collected battery information to Determination of battery state of charge; Step 2) Vehicle modeling: The planetary gear type hybrid electric vehicle contains five dynamic components, which are the engine, battery, two electric generators and wheels. It also has the function of electronic continuously variable transmission. According to the vehicle mechanical coupling and electronic coupling relationship, the system dynamic equation is written, and the dynamic equation is decoupled to obtain the state space model of the system, as shown in formula (1): <mrow> <mfenced open = "{" close = "}"> <mtable> <mtr> <mtd> <mrow> <mi>x</mi> <mo>=</mo> <mo>&amp;lsqb;</mo> <mtable> <mtr> <mtd> <msub> <mi>p</mi> <mi>p</mi> </msub> </mtd> <mtd> <msub> <mi>v</mi> <mi>p</mi> </msub> </mtd> <mtd> <msub> <mi>p</mi> <mn>2</mn> </msub> </mtd> <mtd> <msub> <mi>v</mi> <mn>2</mn> </msub> </mtd> <mtd> <mrow> <msub> <mi>SOC</mi> <mn>2</mn> </msub> </mrow> </mtd> <mtd> <msub> <mi>p</mi> <mi>f</mi> </msub> </mtd> <mtd> <msub> <mi>v</mi> <mi>f</mi> </msub> </mtd> </mtr> </mtable> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>u</mi> <mo>=</mo> <mo>&amp;lsqb;</mo> <mtable> <mtr> <mtd> <msub> <mi>u</mi> <mn>2</mn> </msub> </mtd> <mtd> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> </mtd> </mtr> </mtable> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mover> <mi>x</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>u</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>u</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>v</mi> <mi>p</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>a</mi> <mi>p</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>v</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> <mo>-</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msub> <mi>&amp;rho;C</mi> <mrow> <mi>D</mi> <mn>2</mn> </mrow> </msub> <msub> <mi>A</mi> <mn>2</mn> </msub> <msup> <msub> <mi>v</mi> <mn>2</mn> </msub> <mn>2</mn> </msup> <mo>/</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>-</mo> <mn>9.8</mn> <mi>&amp;mu;</mi> <mo>-</mo> <mn>9.8</mn> <msub> <mi>sin&amp;theta;</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mfrac> <mrow> <msub> <mi>V</mi> <mrow> <mi>max</mi> <mn>2</mn> </mrow> </msub> <mo>-</mo> <msqrt> <mrow> <msubsup> <mi>V</mi> <mrow> <mi>max</mi> <mn>2</mn> </mrow> <mn>2</mn> </msubsup> <mo>-</mo> <mn>4</mn> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> <msub> <mi>R</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> </mrow> </msqrt> </mrow> <mrow> <mn>2</mn> <msub> <mi>R</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> <msub> <mi>Q</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <msub> <mi>v</mi> <mi>f</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>a</mi> <mi>f</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>p</mi> </msub> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>v</mi> <mi>min</mi> </msub> <mo>)</mo> </mrow> </mrow> </msup> <mo>)</mo> <mo>+</mo> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <msub> <mi>&amp;beta;</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>p</mi> </msub> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>v</mi> <mi>max</mi> </msub> <mo>)</mo> </mrow> </mrow> </msup> <mo>)</mo> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mi>f</mi> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mi>f</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>f</mi> </msub> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>v</mi> <mi>min</mi> </msub> <mo>)</mo> </mrow> </mrow> </msup> <mo>)</mo> <mo>+</mo> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <msub> <mi>&amp;beta;</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mi>f</mi> </msub> <mo>(</mo> <mi>&amp;tau;</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>v</mi> <mi>max</mi> </msub> <mo>)</mo> </mrow> </mrow> </msup> <mo>)</mo> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> In the formula, x is the state quantity, u is the control quantity, the parameters p _p , v _p , p _f and V _f are the front vehicle position, the front vehicle speed, the rear vehicle position and the rear vehicle speed, and the parameters p ₂ , v ₂ and SOC ₂ is the position, speed and state of charge of the battery, the parameters u ₂ and P _batt2 are the driving acceleration of the vehicle and the charging and discharging power of the battery of the vehicle, and the parameters ρ, C _D2 , A ₂ , m ₂ , g, μ and θ ₂ are air density, air resistance coefficient of self-vehicle, windward area of self-vehicle, mass of self-vehicle, acceleration of gravity, rolling resistance coefficient and road gradient of self-vehicle, V _OC , R _batt and Q _batt are battery open circuit voltage, internal resistance and Capacity, due to the inertia of the vehicle in the prediction interval, it is assumed that the acceleration of the vehicle ahead is constant. If the speed of the vehicle ahead is greater than the maximum value or less than a certain value, the acceleration of the vehicle ahead is 0. If the traffic light is red in front, the speed of the vehicle is assumed to be 0. The vehicle in front stops at the position of the traffic signal light, and the speed mode of the vehicle's start and stop adopts the experimental curve and is measured by the characteristics of the actual driver; The vehicle's fuel economy evaluation adopts the Weilan's linear model, as shown in formula (2): <mrow> <msub> <mover> <mi>m</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>f</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mover> <mi>m</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>f</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>q</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>&amp;ap;</mo> <msub> <mi>c</mi> <mi>f</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>q</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> In the formula, m _f is the fuel consumption rate, the parameter P _req is the required power of the vehicle, and c _f is a constant parameter; Step 3) Formulate control strategy: The steps of predicting the optimal control strategy based on the energy management model of hybrid electric vehicles based on decentralized control are as follows: first, detect the state of the own vehicle, the rear vehicle and the front vehicle, including position, speed and acceleration information, and then use the established The mathematical model and formulaic control strategy are used to solve the optimal control problem, and finally the first control quantity of the optimal control sequence is applied to the system. Since the model predictive control is an interval optimal control, the optimal control quantity obtained by it is is the sequence whose quantity is the prediction interval divided by the sampling interval. The first control quantity of the optimal control sequence is the closest to the actual state, so it is used as the actual control quantity; The definition of the optimal control problem is shown in formula (3): <mrow> <mfenced open = "{" close = "}"> <mtable> <mtr> <mtd> <mrow> <mi>min</mi> <mi>i</mi> <mi>m</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </mtd> <mtd> <mrow> <mi>J</mi> <mo>=</mo> <msubsup> <mo>&amp;Integral;</mo> <mi>t</mi> <mrow> <mi>t</mi> <mo>+</mo> <mi>T</mi> </mrow> </msubsup> <mi>L</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>(</mo> <mrow> <mi>&amp;tau;</mi> <mo>,</mo> <mi>t</mi> </mrow> <mo>)</mo> <mo>,</mo> <mi>u</mi> <mo>(</mo> <mrow> <mi>&amp;tau;</mi> <mo>|</mo> <mi>t</mi> </mrow> <mo>)</mo> <mo>)</mo> </mrow> <mi>d</mi> <mi>&amp;tau;</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>s</mi> <mi>u</mi> <mi>b</mi> <mi>j</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> <mi> </mi> <mi>t</mi> <mi>o</mi> </mrow> </mtd> <mtd> <mrow> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> <mi>min</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>|</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> <mi>max</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mrow> <msub> <mi>u</mi> <mrow> <mn>2</mn> <mi>min</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>u</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>&amp;tau;</mi> <mo>|</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>u</mi> <mrow> <mn>2</mn> <mi>max</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> In the formula, T is the prediction interval, and the parameters P _batt2min , P _batt2max , u _2max and u _2min are control quantity constraints; The definition of the evaluation function is shown in formula (4): <mrow> <mfenced open = "{" close = "}"> <mtable> <mtr> <mtd> <mrow> <mi>L</mi> <mo>=</mo> <msub> <mi>w</mi> <mi>x</mi> </msub> <msub> <mi>L</mi> <mi>x</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>y</mi> </msub> <msub> <mi>L</mi> <mi>y</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>z</mi> </msub> <msub> <mi>L</mi> <mi>z</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>d</mi> </msub> <msub> <mi>L</mi> <mi>d</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>e</mi> </msub> <msub> <mi>L</mi> <mi>e</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>f</mi> </msub> <msub> <mi>L</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>r</mi> </msub> <msub> <mi>L</mi> <mi>r</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>s</mi> </msub> <msub> <mi>L</mi> <mi>s</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>x</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>-</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <msub> <mi>&amp;rho;C</mi> <mrow> <mi>D</mi> <mn>2</mn> </mrow> </msub> <msub> <mi>A</mi> <mn>2</mn> </msub> <msup> <msub> <mi>v</mi> <mn>2</mn> </msub> <mn>2</mn> </msup> <mo>/</mo> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>-</mo> <mn>9.8</mn> <mo>*</mo> <mi>&amp;mu;</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>y</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>v</mi> <mi>d</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>z</mi> </msub> <mo>=</mo> <mn>0.0874</mn> <mo>*</mo> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>*</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>*</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>/</mo> <mn>1000</mn> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>/</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mn>0.5</mn> <mo>*</mo> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>*</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>*</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>/</mo> <mn>1000</mn> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>d</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>SOC</mi> <mn>2</mn> </msub> <mo>-</mo> <msub> <mi>SOC</mi> <mi>d</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>e</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>*</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <mo>*</mo> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>/</mo> <mn>1000</mn> <mo>-</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> <mi>t</mi> <mn>2</mn> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>f</mi> </msub> <mo>=</mo> <mrow> <mo>(</mo> <mo>-</mo> <mi>ln</mi> <mo>&amp;lsqb;</mo> <msub> <mi>SOC</mi> <mn>2</mn> </msub> <mo>-</mo> <mn>0.6</mn> <mo>&amp;rsqb;</mo> <mo>-</mo> <mi>ln</mi> <mo>&amp;lsqb;</mo> <mn>0.8</mn> <mo>-</mo> <msub> <mi>SOC</mi> <mn>2</mn> </msub> <mo>&amp;rsqb;</mo> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>r</mi> </msub> <mo>=</mo> <mo>-</mo> <mi>ln</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mi>f</mi> </msub> <mo>-</mo> <msub> <mi>d</mi> <mi>d</mi> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mi>ln</mi> <mrow> <mo>(</mo> <msub> <mi>d</mi> <mi>p</mi> </msub> <mo>-</mo> <msub> <mi>d</mi> <mi>d</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>L</mi> <mi>s</mi> </msub> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <mn>2</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow> In the formula, SOC _d is the state of charge of the target battery, V _d is the target speed of the vehicle, and its value is the optimal constant speed fuel economy speed of the vehicle, W _x , W _y , W _z , W _d , We _e , W _f , W _r and W _s are weight coefficients, d _d is the minimum vehicle distance, the evaluation function is set to float above the minimum vehicle distance, so as to increase the control degree of freedom and improve vehicle fuel economy, and the barrier function is used to deal with system state constraints, d _f is the distance between the rear vehicle and its own vehicle, d _p is the distance between the front vehicle and its own vehicle; Step 4) Online optimal control: In order to ensure the real-time optimal performance of the system, the numerical fast solution method based on the Hamiltonian equation is used to solve the above optimal control problem, and the minimum value principle is used to convert the optimal control problem into a two-point edge When dealing with the differential equations and algebraic equations related to the Hamiltonian function, the partial space method is used to solve the value problem, which is a GMRES solution method; At each sampling moment, first measure the front vehicle position, self-vehicle position, rear vehicle position, front vehicle speed, self-vehicle speed, rear vehicle speed, front-vehicle acceleration, self-vehicle acceleration, rear-vehicle acceleration, and self-vehicle battery charge Status real-time status signal, secondly use global positioning system, vehicle-to-vehicle communication system, vehicle-road communication system and intelligent transportation system to predict the status of vehicles and surrounding environment in a certain range in the future, including the acceleration of the front vehicle, the acceleration of the rear vehicle, the distance between the front vehicle and the vehicle, The distance between the following vehicle and the self-vehicle is again based on the established vehicle model and the optimal control problem, using the above numerical fast solution method to solve the optimal control sequence in the prediction interval, and applying the first control quantity of the optimal control sequence in the prediction interval to Then, at the next sampling time, the vehicle advances the prediction interval one step forward, and this cycle repeats to achieve online optimal control.