Tandem hybrid electric vehicle energy management method based on event-triggered multi-target MPC

Abstract

The invention discloses a series hybrid electric vehicle energy management method based on event-triggered multi-target MPC, and belongs to the technical field of hybrid electric vehicle energy management and motion control. Firstly, a power system and a dynamic system of an automobile are modeled, and an obtained mathematical model is used as a multi-target MPC prediction model. And then selecting an appropriate optimization objective function and system constraints according to a control objective to construct a multi-objective MPC constraint optimization problem. And sampling the system state at the current moment, constructing and solving a finite time domain multi-target MPC constraint optimization problem to obtain an optimal control sequence predicted at the current moment, and acting the first s optimal control on the vehicle system. And finally, designing an event triggering mechanism, obtaining a new system state at a new triggering moment, updating the multi-target MPC constraint optimization problem, and carrying out rolling iteration until the control process is finished. According to the method, the stability of the power of the power battery pack and the engine of the series hybrid electric vehicle is ensured, and the calculation efficiency of the optimization problem is higher.

Description

Technical Field

[0001] The invention belongs to the technical field of energy management and motion control of hybrid electric vehicles, and in particular is an energy management method for series hybrid electric vehicles based on event-triggered multi-objective MPC. Background Art

[0002] The rapid development of the automobile industry has brought convenience to people's lives, but it has also caused negative environmental and resource problems, such as the deteriorating air quality and the increasingly depleted oil and gas resources. In recent years, energy conservation and emission reduction have become a topic of concern to the international community. As an effective solution to the energy crisis, new energy vehicles have shown rapid growth in the automobile market. Among them, electric vehicles are increasingly recognized by the market due to their many technical advantages such as environmental protection, economy, energy saving, and low noise. However, the cost of electric vehicles remains high because the energy storage technology with high energy density and high power density has not been effectively solved. In the past decade, hybrid vehicles have become more and more popular in the automobile market because they combine traditional fuel vehicles with electric vehicles and have the advantages of both electric vehicles and traditional vehicles. In particular, series hybrid vehicles have the advantages of high performance, low emissions, and flexible working methods, which greatly reduces fuel consumption.

[0003] For hybrid vehicles, the power system usually contains two or more energy sources for propulsion, such as an internal combustion engine and a battery. Therefore, their energy management is more complex than that of traditional vehicles. The energy management strategy of hybrid vehicles can achieve the reasonable allocation of power to multiple power sources according to different operating conditions, reduce the fuel consumption of hybrid vehicles, and achieve the goal of energy conservation and emission reduction. Reasonable energy management strategy is the key to improving the fuel economy of the whole vehicle. Therefore, energy management strategy has received extensive attention and research. Considering issues such as energy consumption, economic cost, and weight, the research on energy management of hybrid vehicles has very important engineering significance.

[0004] The energy management strategy of hybrid vehicles can usually effectively allocate and optimize the power of the power battery and the engine, ensure the stability of the battery operation, meet the power requirements under different working conditions, and reduce fuel consumption. For the energy management problem of hybrid vehicles, the main idea is to reasonably allocate power while meeting the state and input constraints. At present, there are two main types of hybrid vehicle energy management strategies:

[0005] 1) Rule-based energy management strategies, such as P. The rule-based multi-layer energy allocation strategy proposed by et al. and the fuzzy rule energy allocation strategy proposed by Chun-Yan L et al., but the rule-based method usually cannot guarantee the optimality of energy saving;

[0006] 2) Energy management strategy based on optimization. Stefano Di Cairano et al. used model predictive control to manage the energy distribution of series hybrid electric vehicles, and used batteries to adjust the transition between different working points to smooth the engine transients and improve efficiency. Liangfei Xu et al. proposed a dual-loop Pareto optimal strategy based on dynamic programming to achieve minimum fuel economy and system durability.

[0007] The above-mentioned optimization-based energy management strategies can only optimize energy economy but cannot optimize motion control at the same time, which greatly limits the scope of application of energy management strategies. In particular, these strategies cannot guarantee the energy allocation problem under different working conditions. In addition, it can be found that most of these methods do not consider the actual constraints of the system state and the control input constraints. However, in practical applications, the capabilities of energy control input and motion control input are limited. Energy management strategies based on optimization usually have high requirements for computing resources, which undoubtedly brings new challenges; on the other hand, these control methods only meet certain performance indicators such as minimum energy consumption during the control process. Therefore, there is an urgent need for a method that can take these factors into consideration while designing energy management strategies, including energy economy, motion control performance, vehicle motion constraints, and computing resources.

[0008] The event-triggered multi-objective model predictive control (MPC) method can achieve a balance between multiple performance indicators while satisfying the constraints. It is worth noting that although Weida Wang, Yonggang Liu, Liang Lia, etc. have studied the use of multi-objective optimization methods to solve the problems related to hybrid vehicle energy management. However, these methods do not consider the motion control performance indicators and the constraints of limited computing resources at the same time, so it is difficult to ensure the balance between system motion control, computational complexity and energy management in practical applications. In addition, due to different requirements for power distribution under different working conditions, it is obvious that the introduction of event-triggered prediction mechanism can make predictive adjustments, management and optimization of power distribution, and save computational effort by further reducing the number of times the MPC optimization problem is solved. Summary of the invention

[0009] The purpose of the present invention is to provide an energy management method for a series hybrid electric vehicle based on an event-triggered multi-objective MPC while satisfying constraints and power requirements, so as to obtain optimal energy consumption economy and motion control, so as to solve the problems of actual constraints, limited computing resources, failure to meet given performance and high energy consumption in the energy management control of series hybrid electric vehicles.

[0010] An energy management method for a series hybrid electric vehicle based on event-triggered multi-objective MPC includes the following steps:

[0011] Step 1, modeling the power system and dynamic system of the series hybrid electric vehicle to obtain a whole vehicle system model;

[0012] (1) The longitudinal dynamics model of the series hybrid vehicle is obtained by modeling the dynamics system, which is expressed as:

[0013]

[0014] Among them, m v is the mass of the vehicle, v is the longitudinal speed of the vehicle; the traction force F of the hybrid vehicle v Provided by permanent magnet synchronous motor; F R =f R m v gcosθ is the rolling resistance, f R is the rolling resistance coefficient, θ is the road inclination angle; F D =0.5A f C D v 2 is the air resistance, A f is the windward area, C D is the air resistance coefficient; F G =m v g sinθ is the climbing resistance; g is the gravitational constant;

[0015] (2) The energy flow model of the series hybrid electric vehicle power system is as follows:

[0016] The energy flow of the power system includes: the energy from the combustion of fuel in the internal combustion engine is provided to the engine, and the engine provides energy to the permanent magnet synchronous generator; the electric energy output by the permanent magnet synchronous generator and the power battery pack is input into the electric motor after passing through the converter, or the energy output by the permanent magnet synchronous generator is input into the power battery pack for storage through the converter; the electric motor transmits energy to the transmission system, and drives the series hybrid vehicle through the transmission system.

[0017] The overall energy flow model of the power system is:

[0018]

[0019] Among them, P t is the transmission power of the transmission system, η i is the energy conversion efficiency constant of the converter, η m is the motor efficiency constant, η t is the constant transmission efficiency constant of the transmission system, sign(·) is the sign function, P bis the power of the battery pack, P g is the permanent magnet synchronous generator power, η b It is the charging and discharging efficiency constant of the power battery pack.

[0020] (3) Establish a vehicle system model based on the dynamics model and power model;

[0021] Permanent magnet synchronous generator power P g =F g v, power battery pack power P b =F b v, where the traction force of the permanent magnet synchronous generator is F g =m v u g , the traction force of the power battery pack is F b =m v u b .

[0022] Select the vehicle system state as x(t) = [s(t) v(t) SOC(t) m f (t) F g (t) F b (t)] T , the control input is u(t)=[u g u b ] T , then the whole vehicle system model including motion control and power system models is obtained as:

[0023]

[0024] Among them, P v (t) = P t (t)+P h (t), P v (t) is the traction power, P h (t) is the braking energy consumption. The battery pack state of charge SOC∈[0,1] represents the remaining battery power, U oc is the open circuit voltage of the battery, R is the resistance of the battery pack, C max is the rated capacity of the battery; m f (t) is the fuel flow rate of the internal combustion engine, is the initial value of fuel flow rate when the internal combustion engine is idling, q is the lower calorific value of gasoline, α f is the energy conversion constant, and s(t) is the vehicle position.

[0025] The above vehicle system is expressed by a general nonlinear function:

[0026]

[0027] in,

[0028] Step 2: construct the objective function of the multi-objective MPC constrained optimization problem based on the vehicle system model;

[0029] The objective function of the multi-objective MPC constrained optimization problem at the current time t is:

[0030]

[0031] Where T is the prediction time domain, x(s|t) is the prediction of the error state at time t for time t+s, and u(s|t) is the prediction of the control input at time t for time t+s. L(x(s|t), u(s|t)) is the stage objective function

[0032] L(x(s|t),u(s|t))

[0033] =α f m f (s|t)+α b |SOC(s|t)-SOC r | 2 +α v |v(s|t)-v d (s|t)| 2

[0034] +α s |s(s|t)-s d (s|t)| 2

[0035] Among them, α f , α b , α v , α s is the weight coefficient, v d (s|t) is the target speed, s d (s|t) is the target position, SOC r It is the reference value of battery status.

[0036] Step 3: Set the constraints of the multi-objective MPC constrained optimization problem, predict the future dynamics of the system according to the model, solve the multi-objective MPC constrained optimization problem at time t, and obtain the optimal control sequence predicted at time t;

[0037] The constraints include:

[0038] 1) Initial state, x(0|t)=x(t).

[0039] 2) Speed ​​constraint, 0≤v≤v max , where v max is the maximum speed of the vehicle.

[0040] 3) Traction constraint, |F v (s|t)|≤F max , where F max For maximum traction.

[0041] 4) Battery state constraint, SOC min ≤SOC(s|t)≤SOC max , where SOC min , SOC max are the minimum and maximum battery capacities respectively.

[0042] 5) Generator power constraint, P g，min ≤P g (s|t)≤P g，max , where P g，max is the maximum power of the generator, P g，min is the minimum power.

[0043] 6) Battery power constraint, P b，min ≤P b (s|t)≤P b，max , where P b，max is the maximum power of the battery, P b，min is the minimum power.

[0044] 7) Control input constraints, |u a (s|t)|≤U max ,|u b (s|t)|≤U max .

[0045] Set the prediction time domain T and sampling interval δ of the multi-objective MPC, the weight parameters of the objective function and other corresponding parameters. Set the reference position s d (t) and trajectory velocity v d (t), the initial state of the hybrid vehicle is set to x(0), and based on the above constraints, the specific form of the optimization problem at time t is solved as follows:

[0046]

[0047] stx(0|t)=x(t)

[0048]

[0049] 0≤v≤v max

[0050] |F v (s|t)|≤F max

[0051] SOCmin ≤SOC(s|t)≤SOC max

[0052] P g，min ≤P g (s|t)≤P g，max

[0053] P b，min ≤P b (s|t)≤P b，max

[0054] |u a (s|t)|≤U max

[0055] |u b (s|t)|≤U max

[0056] Step 4: Apply the first s optimal control sequences to the vehicle system and design an event trigger mechanism to determine the next trigger time t according to the event trigger mechanism. next ;

[0057] The event triggering mechanism is as follows

[0058]

[0059] Among them, t next is the next triggering moment, x(t+s) is the time at u * (s|t), s∈[t, t next ] is the actual state after acting on the vehicle system, and the constant σ is the trigger threshold. * (s|t),u * (s|t) is the predicted optimal state and control input.

[0060] Step 5: When the new trigger time t next When it arrives, the new sampled state is used as the initial state to construct the multi-objective MPC constrained optimization problem at the new moment, and then return to step 3 and perform rolling iteration until the control process ends.

[0061] The beneficial effects of the present invention are mainly manifested in:

[0062] (1) The present invention uses an event-triggered multi-objective MPC method to achieve motion control and energy management optimization of a series hybrid electric vehicle. The designed objective function takes into account the vehicle's motion control objectives, battery energy consumption, and fuel energy consumption. By optimizing this objective, the power distribution between the power battery pack and the engine can be achieved while satisfying motion control. At the same time, the specific performance indicators can be further optimized by setting the weight parameters.

[0063] (2) The event-triggered multi-objective MPC motion control and energy management strategy designed by the present invention takes the actual input range and state range as hard constraints of the optimization problem, which is more in line with practical applications; especially under different working conditions, such as starting and stopping, going uphill, and accelerating, the power battery pack and the engine can provide stable power.

[0064] (3) The event-triggered multi-objective MPC method designed in the present invention can obtain the optimal control input in a shorter prediction time domain while satisfying the constraints, and reduce the number of times to solve the optimization problem, which greatly saves the amount of calculation, so that the proposed method can be applied in real time on hybrid vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a framework diagram of energy flow and motion control of a series hybrid electric vehicle of the present invention;

[0066] Figure 2 The present invention is a flow chart of the energy management method of a series hybrid electric vehicle based on event-triggered multi-objective MPC. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments are exemplary and are intended to explain the present invention, but should not be understood as limiting the present invention.

[0068] The present invention proposes an energy management method for a series hybrid electric vehicle based on event-triggered multi-objective MPC, such as Figure 2 As shown in the figure, the principle is to model the power system and dynamic system of the car, mainly including the power battery pack, engine, generator and car dynamics model. Then, the obtained mathematical model is used as the prediction model of multi-objective MPC, and then the appropriate optimization objective function and system constraints are selected according to the control objective to construct a multi-objective MPC constrained optimization problem. Sample the system state at the current moment, construct a finite time domain multi-objective MPC constrained optimization problem; design an event trigger mechanism; solve the constrained optimization problem, obtain the optimal control sequence predicted at the current moment, and determine the next trigger time t according to the event trigger mechanism. next , the first s optimal controls are applied to the vehicle system; the new system state is obtained at the new triggering moment and the multi-objective MPC constrained optimization problem is updated, and the rolling iteration is performed until the control process ends.

[0069] like Figure 1As shown, the implementation mode of the present invention is mainly divided into two parts. The first part is to model the power system and dynamic system of the automobile, mainly including the power battery pack, engine, generator and automobile dynamics model, etc.; the second part is to use the mathematical model obtained in the first part as a prediction model of the multi-objective MPC, and then select the appropriate optimization objective function and system constraints according to the control objective to construct a multi-objective MPC constrained optimization problem. In addition, an event trigger mechanism is constructed to solve an MPC optimization problem once and apply multiple optimal control inputs to the vehicle system.

[0070] Taking a series hybrid electric vehicle as an example, the specific steps of the method of the present invention are:

[0071] Step 1: Modeling of the vehicle's power system and dynamics system and selection of corresponding parameters;

[0072] Step 1.1: The longitudinal dynamics model of a series hybrid vehicle can be expressed as:

[0073]

[0074] Among them, m v =1500kg is the mass of the vehicle, v is the longitudinal velocity of the vehicle, F R =f R m v g cosθ is the rolling resistance, f R =0.01 is the rolling resistance coefficient, θ is the road inclination angle; F D =0.5A f C D v 2 is the air resistance, A f is the windward area, C D is the air resistance coefficient, A f C D =0.95; F G =m v g sinθ is the climbing resistance. The traction of the hybrid vehicle is provided by a permanent magnet synchronous motor.

[0075]

[0076] Among them, P v =P t +P h is the traction power, P h <0 means braking energy loss, P t is the transmission power of the transmission system.

[0077] Step 1.2: Determine the power balance equation of the transmission system's transmission power and motor power as:

[0078]

[0079] Among them, P m is the motor mechanical power, η t =0.95 is a constant transmission efficiency constant, and sign(·) is a sign function.

[0080] Step 1.3: Determine the power balance relationship between the DC converter and the motor, that is

[0081]

[0082] Among them, P i is the converter power, η m =0.96 is the motor efficiency constant.

[0083] Step 1.4: Determine the power balance relationship between the converter, permanent magnet synchronous generator and power battery pack:

[0084]

[0085] Among them, η i =0.96 is the energy conversion efficiency constant of the converter, P b is the power of the battery pack, P g is the permanent magnet synchronous generator power.

[0086] Step 1.5: The energy between the engine and the permanent magnet synchronous generator is modeled as follows:

[0087] P g =η g P e

[0088] Among them, η g =0.94 is the efficiency constant of the permanent magnet synchronous generator, P e The power provided to the engine.

[0089] Step 1.6: The relationship between the internal combustion engine fuel flow and the permanent magnet synchronous generator can be modeled as

[0090]

[0091] in, is the initial value of fuel flow rate when the internal combustion engine is idling, q = 45 MJ / kg is the lower calorific value of gasoline, α f =0.35 is the energy conversion constant.

[0092] Step 1.7: Determine the power battery pack state estimation model as follows

[0093]

[0094] Where: Battery pack state of charge SOC∈[0,1] represents the remaining battery capacity, C max =5Ah is the rated capacity of the battery, I is the charge and discharge current, when charging, I<0, when discharging, I>0, η b =0.96 is the charge and discharge efficiency constant. According to Kirchhoff's voltage law

[0095] U=U oc -IR

[0096] And battery output power

[0097] P b =UI=U oc I-RI 2

[0098] The estimated state of the battery pack can be obtained

[0099]

[0100] Among them, U oc =300V is the open circuit voltage of the battery, R=90Ω is the resistance of the battery pack, U is the closed circuit voltage of the battery,

[0101] Step 1.8: According to steps 1.1-1.7, the overall energy flow model can be obtained

[0102]

[0103] Step 1.9: The permanent magnet synchronous generator power and battery power can be respectively expressed by P g =F g v and P b =F b v is calculated, where F g =m v u g , F b =m v u b Select the vehicle system state as x(t) = [s(t) v(t) SOC(t) m f (t) F g (t)F b (t)] T , the control input is u(t)=[u g u b ] T , the whole vehicle system model including motion control and power system models can be obtained

[0104]

[0105] Among them, Pv (t) = P t (t)+P h (t). The above vehicle system can be expressed by a general nonlinear function:

[0106]

[0107] in,

[0108] Step 2: Implementation of motion control and energy management strategies based on multi-objective MPC;

[0109] Step 2.1: Set the objective function of the multi-objective MPC constrained optimization problem at time t to:

[0110]

[0111] Where T is the prediction time domain, x(s|t) is the prediction of the error state at time t for time t+s, and u(s|t) is the prediction of the control input at time t for time t+s. L(x(s|t), u(s|t)) is the stage objective function

[0112] L(x(s|t),u(s|t))

[0113] =α f m f (s|t)+α b |SOC(s|t)-SOC r | 2 +α v |v(s|t)-v d (s|t)| 2

[0114] +α s |s(s|t)-s d (s|t)| 2

[0115] Among them, α f =1,α b =1,α v =1,α s =1 is the weight coefficient, v d (s|t) is the target speed, s d (s|t) is the target position, SOC r =0.7 is the reference value of battery status.

[0116] Step 2.2: Set the constraints of the multi-objective MPC optimization problem.

[0117] Initial state, x(0|t) = x(t) = [000.70.120.10.1]T .

[0118] Speed ​​constraint, 0≤v≤v max , where v max =30m / s is the maximum speed of the vehicle.

[0119] ·Traction constraint, |F v (s|t)|≤F max , where F max For maximum traction.

[0120] Battery state constraint, SOC min ≤SOC(s|t)≤SOC max , where SOC min =0.4, SOC max =0.8 are the minimum and maximum battery capacities respectively.

[0121] Generator power constraint, P g，min ≤P g (s|t)≤P g，max , where P g，max =90kW is the maximum power of the generator, P g，min =0 is the minimum power.

[0122] Battery power constraint, P b，min ≤P b (s|t)≤P b，max , where P b，max is the maximum power of the battery, P b，min is the minimum power.

[0123] ·Control input constraints, |u a (s|t)|≤U max =1m / s 3 ,|u b (s|t)|≤U max =1m / s 3 .

[0124] Step 2.3: Set the prediction time domain T = 20, sampling interval δ = 0.5s, weight parameters of the objective function and other corresponding parameters of the multi-objective MPC.

[0125] Step 2.4: Set the reference position s d (t) trajectory speed v d (t), and the initial state of the HEV is set to x(0).

[0126] Step 2.5: Based on the model prediction of the future dynamics of the system, solve the multi-objective MPC constraint optimization problem at time t and obtain the optimal control sequence predicted at time t. The specific form of the optimization problem at time t is:

[0127]

[0128] stx(0|t)=x(t)

[0129]

[0130] 0≤v≤v max

[0131] |F v (s|t)|≤F max

[0132] SOC min ≤SOC(s|t)≤SOC max

[0133] P g，min ≤P g (s|t)≤P g，max

[0134] P b，min ≤P b (s|t)≤P b，max

[0135] |u a (s|t)|≤U max

[0136] |u b (s|t)|≤U max

[0137] Step 2.6: Build the event triggering mechanism as follows

[0138]

[0139] Among them, t next is the next triggering moment, x(t+s) is the time at u * (s|t), s∈[t, t next ] is the actual state after acting on the system, and the constant σ=2.5 is the trigger threshold.

[0140] Step 2.7: Calculate the first s optimal control u * (s|t), s∈[t, t next ] acts on the system until a new triggering time t nextWhen it arrives, the new sampled state is used as the initial state to construct the multi-objective MPC constrained optimization problem at the new moment, and then return to step 2.5 and iterate until the control process ends.

Claims

1. A series hybrid electric vehicle energy management method based on event-triggered multi-objective MPC, characterized in that: The following steps are involved: Step 1, modeling the power system and dynamic system of the series hybrid electric vehicle to obtain a whole vehicle system model; Permanent magnet synchronous generator power P g =F g v, power battery pack power P b =F b v, where F g =m v u g ,F b =m v u b Select the vehicle system state as x(t) = [s(t)v(t) SOC(t) m f (t) F g (t) F b (t)] T , the control input is u(t)=[u g u b ] T , then the whole vehicle system model including motion control and power system models is obtained as: Among them, P v (t) = P t (t)+P h (t), P v (t) is the traction power, P h (t) is the braking energy consumption; the battery pack state of charge SOC∈[0,1] represents the remaining battery power, U oc is the open circuit voltage of the battery, R is the resistance of the battery pack, C max is the rated capacity of the battery; m f (t) is the fuel flow rate of the internal combustion engine, is the initial value of fuel flow rate when the internal combustion engine is idling, q is the lower calorific value of gasoline, α f is the energy conversion constant, s(t) is the vehicle position; m v is the mass of the vehicle, v is the longitudinal velocity of the vehicle; F R =f R m v g cosθ is the rolling resistance, f R is the rolling resistance coefficient, θ is the road inclination angle; F D =0.5A f C D v 2 is the air resistance, A f is the windward area, C D is the air resistance coefficient; F G =m v g sinθ is the climbing resistance; The above vehicle system is expressed by a general nonlinear function: in, Step 2: construct the objective function of the multi-objective MPC constrained optimization problem based on the vehicle system model; The objective function of the multi-objective MPC constrained optimization problem at the current time t is: Where T is the prediction time domain, x(s|t) is the prediction of the error state at time t for time t+s, and u(s|t) is the prediction of the control input at time t for time t+s; L(x(s|t),u(s|t)) is the stage objective function L(x(s|t),u(s|t)) =α f m f (s|t)+α b |SOC(s|t)-SOC r | 2 +α v |v(s|t)-v d (s|t)| 2 +α s |s(s|t)-s d (s|t)| 2 Among them, α f ,α b ,α v ,α s is the weight coefficient, v d (s|t) is the target speed, s d (s|t) is the target position, SOC r is the battery status reference value; Step 3: Set the constraints of the multi-objective MPC constrained optimization problem, predict the future dynamics of the system according to the model, solve the multi-objective MPC constrained optimization problem at time t, and obtain the optimal control sequence predicted at time t; Set the prediction time domain T and sampling interval δ of the multi-objective MPC, the weight parameter of the objective function and other corresponding parameters; set the reference position s d (t) and trajectory velocity v d (t), the initial state of the hybrid vehicle is set to x(0), and based on the constraints, the specific form of the optimization problem at time t is solved as follows: stx(0|t)=x(t) 0≤v≤v max |F v (s|t)|≤F max SOC min ≤SOC(s|t)≤SOC max P g,min ≤P g (s|t)≤P g,max P b,min ≤P b (s|t)≤P b,max |u a (s|t)|≤U max |u b (s|t)|≤U max Step 4: Apply the first s optimal control sequences to the vehicle system and design an event trigger mechanism to determine the next trigger time t according to the event trigger mechanism. next ; The event triggering mechanism is as follows Among them, t next is the next triggering moment, x(t+s) is the time at u * (s|t),s∈[t,t next ] is the actual state after acting on the vehicle system, and the constant σ is the trigger threshold; x * (s|t),u * (s|t) is the predicted optimal state and control input; Step 5: When the new trigger time t next When it arrives, the new sampled state is used as the initial state to construct the multi-objective MPC constrained optimization problem at the new moment, and then return to step 3 and perform rolling iteration until the control process ends.

2. The energy management method of a series hybrid electric vehicle based on event-triggered multi-objective MPC according to claim 1, characterized in that: The longitudinal dynamics model of the dynamic system of the series hybrid vehicle is obtained by modeling, which is expressed as: Among them, m v is the mass of the vehicle, v is the longitudinal velocity of the vehicle; F R =f R m v g cosθ is the rolling resistance, f R is the rolling resistance coefficient, θ is the road inclination angle; F D =0.5A f C D v 2 is the air resistance, A f is the windward area, C D is the air resistance coefficient; F G =m v gsinθ is the climbing resistance; the traction force F of the hybrid vehicle v Provided by a permanent magnet synchronous motor.

3. The energy management method of a series hybrid electric vehicle based on event-triggered multi-objective MPC according to claim 1, characterized in that: The energy flow model of the series hybrid electric vehicle power system is: Among them, P t is the transmission power of the transmission system, η i is the energy conversion efficiency constant of the converter, η m is the motor efficiency constant, η t is the constant transmission efficiency constant of the transmission system, sign(·) is the sign function, P b is the power of the battery pack, P g is the permanent magnet synchronous generator power, η b It is the charging and discharging efficiency constant of the power battery pack.

4. The energy management method of a series hybrid electric vehicle based on event-triggered multi-objective MPC according to claim 3, characterized in that: The energy flow of the power system includes: the energy from the combustion of fuel in the internal combustion engine is provided to the engine, and the engine provides energy to the permanent magnet synchronous generator; the electric energy output by the permanent magnet synchronous generator and the power battery pack is input into the electric motor after passing through the converter, or the energy output by the permanent magnet synchronous generator is input into the power battery pack through the converter; the electric motor transmits energy to the transmission system, and drives the series hybrid vehicle through the transmission system.

5. The energy management method of a series hybrid electric vehicle based on event-triggered multi-objective MPC according to claim 1, characterized in that: The constraints include: 1) Initial state, x(0|t) = x(t); 2) Speed ​​constraint, 0≤v≤v max , where v max is the maximum speed of the vehicle; 3) Traction constraint, |F v (s|t)|≤F max , where F max is the maximum traction; 4) Battery state constraint, SOC min ≤SOC(s|t)≤SOC max , where SOC min , SOC max are the minimum and maximum battery capacities respectively; 5) Generator power constraint, P g,min ≤P g (s|t)≤P g,max , where P g,max is the maximum power of the generator, P g,min is the minimum power; 6) Battery power constraint, P b,min ≤P b (s|t)≤P b,max , where P b,max is the maximum power of the battery, P b,min is the minimum power; 7) Control input constraints, |u a (s|t)|≤U max ,|u b (s|t)|≤U max .

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