CN110135632B - PHEV self-adaptive optimal energy management method based on path information - Google Patents

Abstract

The invention discloses a PHEV self-adaptive optimal energy management method based on path information, which comprises the steps of planning a traveling path through a vehicle-mounted navigation system and generating a predicted working condition of a front path; establishing a trip mileage prediction strategy to predict the trip mileage of the user every day; generating a reference SOC based on an SOC planning algorithm through the generated prediction data and the initial SOC; carrying out APMP optimization algorithm: taking the minimum oil consumption as a global optimization target, introducing a collaborative state value, and converting the global optimization problem into a plurality of instantaneous optimization problems with Hamilton operators; optimizing the initial value of the collaborative state by adopting a genetic algorithm; calculating a cooperative state initial value in the MAP graph by using an interpolation method, and correcting the cooperative state initial value in real time according to the working condition information and the reference SOC obtained by the vehicle navigation system; and power distribution is carried out by utilizing a PMP optimization algorithm, and the power is transmitted to each execution component controller through a CAN bus, so that the whole vehicle control of the PHEV is completed.

Description

Adaptive optimal energy management method for PHEV based on path information

technical field

The invention relates to a vehicle control method for a plug-in parallel hybrid electric vehicle, in particular to an adaptive optimal energy management method for a plug-in parallel hybrid electric vehicle based on path information, and belongs to the technical field of new energy vehicle control.

Background technique

With the increasing aggravation of energy crisis, environmental pollution, global warming and the urgent need for energy conservation and emission reduction, the development of new energy vehicles has received more and more attention. A plug-in hybrid electric vehicle (Plug-in Hybrid Electric Vehicle, PHEV) has a battery with a larger capacity than a hybrid electric vehicle (Hybrid Electric Vehicle, HEV), and can obtain electric energy from the grid. PHEV has the advantages of both HEV and battery electric vehicles (BEV). When the battery is fully charged, PHEV is in charge depleting (CD) mode and is mainly driven by a motor, which has the advantages of low fuel consumption and low emissions; When the battery power is low, the PHEV is in charge sustaining mode (Charge Sustaining, CS), and the engine is used as the main power source to drive the vehicle, which has the same driving range as conventional cars and HEVs. PHEV configurations include various forms such as series, parallel and hybrid. The parallel configuration has the advantages of simple structure, easy processing and manufacturing, good power and economy, and its configuration does not involve patent protection. Most PHEVs in my country adopt this configuration. However, there is a mechanical connection between the engine and the wheels of the parallel configuration PHEV, and its economy is greatly affected by the working conditions.

The energy management strategy of plug-in hybrid electric vehicles is a key issue in the design of PHEVs. At present, most PHEVs in actual operation adopt a rule-based threshold control strategy (Rule-based control strategy, RB), which has a small amount of calculation and real-time Good performance, easy to program the vehicle controller. However, the control threshold of the RB strategy is often a fixed set of thresholds, which is poorly adaptable to working conditions. PHEV economy is affected by various factors such as battery state of charge (State of Charge, SOC), vehicle speed, mileage, road slope, temperature, etc., especially by battery SOC, vehicle speed and mileage. When the control threshold of the RB strategy is fixed, it cannot automatically adapt to the influence of changes in working conditions. This can result in premature battery "depletion" (SOC at the minimum allowable value), or a condition where the battery is not fully used at the end of the trip. Studies have shown that both of these situations will increase the fuel consumption of parallel PHEVs and worsen the economy. In addition, since the control threshold is constant, the instantaneous and global fuel consumption is often not optimal under most operating conditions, and even in some low-speed congested operating conditions, the energy consumption of parallel PHEVs will exceed that of traditional internal combustion engine vehicles. Therefore, the traditional threshold control strategy not only cannot adapt to changes in working conditions, but also fails to achieve optimal fuel consumption globally and instantaneously. This is one of the important reasons for the high energy consumption of parallel PHEVs and the inability to realize fuel-saving potential.

At present, many scholars have proposed PHEV energy consumption strategies based on optimal control theory, such as the global optimization dynamic programming algorithm (Dynamic Programming, DP), the instantaneous optimization equivalent fuel consumption minimum algorithm (EquivalentConsumption Minimum Strategy, ECMS) and Pontry Pontryagin's MinimumPrincipal (PMP) etc. On the premise that the working conditions are known, the global optimization algorithm DP can obtain the theoretical optimal solution under the working conditions through inverse solution, at this time, the energy consumption is optimal. However, due to the reverse solution, the premise of the DP algorithm is that the working conditions are known, and the calculation amount is huge, which obviously cannot be directly applied to the energy management of the actual PHEV vehicle. The ECMS algorithm is an instantaneous optimal control algorithm, which can achieve instantaneous optimality, but it is still not globally optimal when the operating conditions change. Moreover, the ECMS algorithm is mainly used in hybrid electric vehicles, and its constraints require that the SOC should maintain balance. Therefore, it is difficult to directly apply to the control of PHEV. The PMP algorithm also belongs to the instantaneous optimal control. Like ECMS, it is not globally optimal when the working conditions change. However, by introducing cooperative state variables, the PMP algorithm can dynamically allocate engine and motor power, and realize online control of SOC consumption rate. Therefore, PMP may not be required to maintain the balance of SOC, which is very suitable for the energy management of PHEV.

From the above analysis, the key problem to be solved in PHEV energy management is to realize the global and instantaneous optimal control of PHEV energy consumption under any working conditions and under the same power consumption, so as to minimize energy consumption. At present, vehicle navigation systems (including intelligent transportation systems, electronic maps, GPS, etc.) can not only provide services such as navigation, road condition query and prediction, but also provide vehicle operating condition data. The PHEV control system can obtain information such as driving mileage, congestion status (vehicle speed distribution) and historical travel mileage from the car navigation system.

Contents of the invention

The present invention provides an adaptive optimal energy management method for plug-in parallel hybrid electric vehicles based on path information. The method is based on the instantaneous optimal PMP algorithm, and obtains future path information through the vehicle navigation system, so that the synergy matrix of the PMP control strategy The state value can be adjusted online based on future path information, driving conditions and battery SOC, to realize the instantaneous and global optimization of energy management of parallel PHEVs, comprehensively improve the energy consumption of parallel PHEVs under different types of working conditions, and give full play to the energy saving of parallel PHEVs potential.

The purpose of the present invention is achieved through the following technical solutions:

A PHEV adaptive optimal energy management method based on path information, comprising the following steps:

Step 1. Driving conditions and mileage prediction:

1.1) Obtain the vehicle location information through the vehicle navigation system and plan the driving route in the electronic map, and at the same time obtain the real-time road condition information of the planned driving route, and generate the predicted working conditions of the ahead route;

1.2) The car navigation system obtains the historical driving data of the vehicle, establishes a travel mileage prediction strategy to predict the daily travel mileage of users, and draws the cumulative average mileage curve of the vehicle;

Step 2. Generate a reference SOC based on the SOC planning algorithm:

Through the forecast data generated in step 1, including the forecast working conditions, the total mileage of the whole day, the mileage of this trip and the initial SOC, the reference SOC is generated based on the SOC planning algorithm;

Step 3, APMP optimization algorithm:

3.1) With the minimum fuel consumption as the global optimization goal, the collaborative state value is introduced, and the global optimization problem is transformed into several instantaneous optimization problems with Hamiltonian operators;

3.2) Optimization of the initial value of the collaborative state: the initial value of the collaborative state is optimized using a genetic algorithm, and a MAP diagram of the initial value of the collaborative state changing with the initial value of the SOC and the travel mileage is established;

3.3) On-line correction of the cooperative state value: Under actual working conditions, the initial value of the cooperative state is obtained in the MAP diagram by interpolation method, and the initial value of the cooperative state is corrected in real time according to the working condition information obtained by the vehicle navigation system and the reference SOC;

3.4) Solve the Hamilton function, use the PMP optimization algorithm to distribute the power, and transmit it to the controllers of each executive component through the CAN bus to complete the vehicle control of the PHEV.

Described a kind of path-based plug-in hybrid electric vehicle self-adaptive optimal energy management method, step 1.2) specific process is: PHEV energy management system records the user's daily vehicle speed-time mileage data, and then carries out vehicle speed The mileage is obtained by the points, and the mileage of the day is counted by hour, and written into the travel mileage characteristic database. The mileage of weekdays and holidays needs to be counted separately; Time period cumulative average mileage curve.

The specific process of step 2 of the PHEV adaptive optimal energy management method based on path information is as follows:

First generate the reference SOC: take the mileage as the abscissa and the SOC as the ordinate, connect the two points (0,SOC _ini ) and (S _t ,SOC _min ) to get the SOC _ref ;

Among them, SOC _ref is the reference SOC; SOC _ini is the initial SOC of the trip; SOC _min is the minimum SOC of the CD mode; S _t is the total travel mileage of the whole day, and S _{t is} the cumulative average mileage of the vehicle obtained from the step 1 according to the current travel time The interpolation in the curve is obtained;

The system will carry out SOC planning before each trip. For a single trip, the calculation formula of SOC _end at the end of this trip is as follows:

Among them, S _i is the mileage of this trip.

Described a kind of path-based method for self-adaptive optimal energy management of plug-in hybrid electric vehicles, step 3.1) takes the minimum fuel consumption as the global optimization goal, introduces the cooperative state value, and converts the global optimization problem into several The specific process of sub-instantaneous optimization problem is:

Taking the minimum fuel consumption as the global optimization problem, the objective function is:

代表汽车在t时刻的瞬时燃油消耗速率，单位：kg/s；Among them: x(t) is the SOC of the vehicle at time t, that is, the state variable of the controlled system; u(t) is the torque distribution ratio at time t, that is, the system control variable, which is the motor torque T _m and the total demand ratio of torque T _dmd ;

Represents the instantaneous fuel consumption rate of the car at time t, unit: kg/s;

The constraints of this optimization problem include:

Among them, f(x(t), u(t), t) is the rate of change of the state variable x(t) of the car at time t, unit: 1/s;

Among them, the first formula represents the state transition equation of the system, and the second formula represents the final value constraint condition of the optimization problem, that is, when the process ends, the SOC of the vehicle cannot be lower than x _min ;

Using Pontryagin's extreme value principle to convert the global optimization problem of formula (2) into several instantaneous optimization problems about the Hamiltonian function by introducing cooperative state quantities: PMP optimization algorithm defines the Hamiltonian function H(x(t),u(t) ,λ(t),t) as the optimization objective of the instantaneous optimization problem, as shown in the following formula:

Among them, λ(t) is the cooperative state quantity; the first item in the Hamiltonian function is the instantaneous fuel consumption of the engine, which is found in the engine universal characteristic MAP diagram according to the engine speed and torque; the second item is the multiplication of the cooperative state value by the SOC Instantaneous change, the cooperative state quantity is a new state quantity introduced by the PMP optimization algorithm, which is related to the driving conditions. In different driving events, the initial value of the cooperative state quantity is different; the minimum value of the Hamilton function at each moment is solved, The obtained optimal control variable sequence is the optimization result, as shown in the following formula:

Its cooperative state transition equation is:

A kind of PHEV self-adaptive optimal energy management method based on path information, step 3.2) collaborative state initial value optimization comprises the following process:

The genetic algorithm is used to solve the initial value of the cooperative state λ ₀ ,

The genetic algorithm takes the minimum fuel consumption as the optimization goal, and takes the initial value of the collaborative state λ ₀ as the individual, and the fitness function of the individual is:

The synergy state value corresponding to the minimum value of the fitness function is the optimization result, and a synergy state value MAP diagram is drawn; before the start of the trip, the system uses the synergy state value MAP diagram interpolation to determine the system State initial value λ ₀ .

Described a kind of PHEV self-adaptive optimal energy management method based on path information, step 3.3) cooperative state value online correction comprises the following process:

According to the reference SOC obtained in step 2, and introduce the SOC penalty factor and the speed penalty factor, so that the actual SOC can follow the reference SOC in real time, and the corrected cooperative state value is calculated by the following formula:

λ(t)=λ ₀ +s(ΔSOC,t)+s(ΔV,t)

λ(t) is affected by two factors: SOC difference ΔSOC and vehicle speed difference ΔV, ΔSOC=SOC-SOC _ref , ΔV=VV _m ;

The penalty factor s(ΔSOC,t) of the SOC difference takes a minimum value when the deviation from the reference SOC is small, and the value should increase rapidly when the deviation from the reference SOC is too large;

When ΔSOC>0, the value of penalty factor s is positive; when ΔSOC<0, the value of penalty factor s is negative; the expression of penalty factor s(ΔSOC,t) is as follows:

The penalty factor s(ΔV,t) of the vehicle speed difference takes a minimum value when the deviation from the average vehicle speed is small, and the value should increase rapidly when the deviation from the average vehicle speed is too large;

When ΔV>0, the value of penalty factor s is negative; when ΔV<0, the value of penalty factor s is positive; the expression of penalty factor s(ΔV,t) is as follows:

Described a kind of PHEV self-adaptive optimal energy management method based on path information, step 3.4) comprises the following process to Hamilton function solution:

Use the numerical solution to solve the Hamiltonian function: first calculate the dynamic equation to obtain the total demand torque, then determine the initial value of the cooperative state by interpolation method according to the travel mileage of this trip and the initial SOC, and determine the SOC difference and vehicle speed difference based on the vehicle state , modify the current cooperative state value; establish a Hamiltonian function to divide the feasible region of the torque distribution ratio u(t) into several parts; if u(t)>0, it means that the motor and the engine drive the vehicle together or the motor drives the vehicle alone; If u(t)<0, it means that the motor is in the generator state; calculate all the Hamiltonian function values after numerical grid division, and find the minimum torque distribution ratio of the corresponding Hamiltonian function.

The present invention has the following beneficial effects:

1) The present invention introduces the vehicle navigation system into PHEV energy management, predicts the road condition characteristics through the vehicle navigation system and makes statistics on the historical travel information of the vehicle, and proposes a SOC planning method (refer to SOC), which solves the problem that the traditional PMP algorithm cannot To adapt to changes in working conditions, fuel consumption is not a global optimal issue.

2) A working condition adaptive PMP control strategy based on SOC feedback is proposed. In order to realize the online control of the real vehicle, the initial value of the cooperative state is solved by using the offline MAP graph. According to the working condition information and reference SOC to correct the cooperative state value, the PMP optimization algorithm is used to reasonably allocate the power consumption, so that the fuel consumption of the PHEV can be close to the theoretical optimal level under any working condition.

Description of drawings

Specific implementations of the present invention will be described in detail below in conjunction with application examples.

Figure 1 is a hardware structure diagram of the parallel PHEV transmission and control system;

Figure 2 is a PHEV adaptive optimal control strategy architecture based on path information;

Figure 3 is an example of Baidu intelligent transportation system route planning and traffic information;

Fig. 4 is the average vehicle speed figure converted according to the real-time traffic system information of Baidu map;

Figure 5(a) is the cumulative average mileage curve of working days;

Figure 5(b) is the cumulative average mileage curve during holidays;

Figure 6 is a schematic diagram of the reference SOC algorithm;

Figure 7 is the WLTC working condition test cycle;

Figure 8 is a MAP diagram of the initial value of the collaborative state;

Figure 9 is the actual SOC and reference SOC drop curve

Figure 10 is the SOC penalty factor curve;

Fig. 11 is average vehicle speed penalty factor curve;

Fig. 12 is a flow chart of solving the Hamiltonian function.

Detailed ways

The invention will be further described below in conjunction with the accompanying drawings. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form.

The application object of the present invention is a parallel configuration PHEV, and Fig. 1 is the hardware structure of its power system and energy management system. The PHEV in this example adopts a coaxial parallel structure. Wherein, the motor is coaxially installed on the input shaft of the automatic transmission, and the battery can be charged by an external charger. PHEV vehicle control system includes: accelerator pedal (including pedal opening sensor), brake pedal (including pedal opening sensor), vehicle controller (HCU), GPS positioning module, remote communication module, engine controller (ECU) , Motor Controller (MCU), Automatic Transmission Controller (TCU), Battery Management Unit (BMU), all components exchange information through the CAN bus. The vehicle controller (HCU) obtains the current location of the vehicle through the GPS module, and communicates remotely with the Intelligent Transportation System (ITS) through the remote communication module to obtain route information. The ITS system includes multiple subsystems such as traffic condition information service, geographic information service, and navigation service. The total mileage, the speed characteristics of each road section, and the slope of the road surface are transmitted to the vehicle controller (HCU) through the telematics module. At the same time, the memory in the HCU of the target vehicle can also store travel data of the vehicle user for a period of time, such as vehicle speed, driving time, vehicle location and other information.

The invention mainly includes three parts: "working condition and mileage prediction algorithm", "reference SOC generation algorithm" and "adaptive PMP (APM) control algorithm". "Working condition and mileage prediction algorithm" obtains working condition information such as the mileage of the forward path, road slope, traffic light signal and vehicle speed distribution through the vehicle navigation system, and generates the predicted working condition of the forward path; obtains the historical working condition data through the vehicle navigation system, and generates Historical travel mileage prediction curve. The reference SOC generation algorithm generates the reference SOC (SOC _ref ) based on the global optimal principle by predicting the operating conditions and predicting the travel mileage. The "APMP optimization algorithm" takes the minimum fuel consumption as the global optimization goal, introduces the cooperative state value, and transforms the global problem into several instantaneous optimization problems with Hamiltonian operators. Using the offline optimization method, the MAP diagram of the initial value of the cooperative state changing with the initial value of SOC and the travel mileage is established. Under actual working conditions, the initial value of the cooperative state is obtained in the MAP diagram by using the interpolation method, and the initial value of the cooperative state is corrected in real time according to the working condition information obtained by the vehicle navigation system and the reference SOC. The PMP optimization algorithm is used for power distribution, so that the fuel consumption of the vehicle is close to the theoretical optimal level.

Example

Fig. 2 is a PHEV self-adaptive optimal control strategy architecture based on path information. In combination with Fig. 1, the specific implementation process of the energy management method proposed by the present invention is introduced as follows:

Step 1: After the vehicle starts, the system performs self-check and initialization. If the driver inputs the travel destination in the vehicle navigation system, the control strategy (method) proposed by the present invention is used to manage the PHEV energy.

Step 2: After the driver enters the travel destination in the car navigation system, the GPS in the car navigation system will obtain the vehicle location information and plan the driving route on the electronic map. At the same time, the real-time traffic conditions of the planned route are obtained in the ITS system, and the smooth, slow, and congestion distances are measured through the electronic map distance measurement function to calculate the congestion ratio of the road conditions. From the distance measurement function and the congestion ratio, the current travel mileage and average vehicle speed information required by the adaptive strategy can be obtained respectively. Figure 3 is an example of Baidu intelligent transportation system route planning and real-time traffic information. Four colors are used to represent the traffic conditions of this road section: dark red represents severe congestion (average speed is less than 15km/h); red represents congestion (average speed is between 15km/h and 25km/h); h～40km/h); green means smooth (average vehicle speed above 40km/h). The vehicle speed information in this system comes from the traffic velocity sensor in the traffic monitoring system or the GPS on the taxi (mobile vehicle). Assuming that the driver's driving route is shown in Figure 3, the real-time road condition information on the route is also displayed. It can also be obtained from the system that the total travel distance of this section of the route is 8.2 kilometers, and the estimated travel time is 20 minutes. The calculated average vehicle speed of this section of the route is 24.6km/h, and the color information is converted into predicted average vehicle speed information, as shown in Figure 4. Among them, deep red represents 5km/h; red represents 20km/h; yellow represents 30km/h; green represents 45km/h.

Step 3: The travel mileage identification module establishes a travel mileage prediction strategy to predict the daily user travel mileage based on the vehicle's historical driving data. Since the travel mileage of private car users with travel rules shows a certain degree of convergence, the statistics and predictions of the travel mileage feature of the present invention are the travel mileage of a single user on different dates and different time periods. The PHEV energy management system records the user's daily driving conditions (vehicle speed-time mileage) data, then integrates the vehicle speed to obtain the mileage, calculates the mileage of the day by hour, and writes it into the travel mileage characteristic database. The mileage of working days and holidays shall be counted separately. When the number of statistical days is sufficient, the convergence condition is used to judge whether the user's travel characteristics are converged. In this example, the 90-day mileage is counted. If the mileage falls within the range of the average value [-5km, +5km] with a probability of 90%, then the travel mileage of the user has convergence characteristics. The convergence of driving characteristics on weekdays and rest days needs to be counted separately. If it does not converge, continue to make statistics until it converges; if it converges, calculate the average mileage of each period of weekdays and holidays, and draw the cumulative average mileage curve of each period of weekdays and holidays. Figure 5(a) and Figure 5(b) are the cumulative average mileage curves of a private car user.

Step 4: The SOC planning module generates a reference SOC curve based on the SOC planning algorithm based on the predicted data, including the total mileage of the whole day, the mileage of this trip, the initial SOC, and the status of electric accessories. The specific generation process is shown in Figure 6 , the process is as follows:

First generate the reference SOC (SOC _ref ), take the mileage as the abscissa and SOC as the ordinate, connect the two points (0, SOC _ini ) and (S _t , SOC _min ) to obtain the linear reference SOC _ref (thin line in Figure 6 ). Among them, SOC _ini is the initial SOC of the trip; SOC _min is the minimum SOC in CD mode; S _t is the total travel mileage of the whole day. S _t is obtained by interpolation from the cumulative average mileage curve obtained in step 3 according to the current travel time. Taking a user as an example, at the beginning of the trip, the system predicts the remaining total mileage of the car on the day based on the current travel time and whether it is a holiday or not through the cumulative average mileage curve. If it is judged that the day is a working day, use the cumulative average mileage curve of the working day in Figure 5(a), set the current travel time as 6:30, then the total travel mileage S _t for the whole day is 39.5km; The time is 16:30, then S _t is 19.8km. The system will carry out SOC planning before each trip. For a single trip, the calculation formula of SOC _end at the end of this trip is as follows:

Where S _i is the mileage of this trip. The example of the reference SOC line for the mileage of this trip is shown in the thick line inside the circle in Figure 6.

Step 5: The APMP optimization module determines the cooperative state value according to the SOC initial value SOC _ini , the travel mileage S _i , the cooperative state initial value λ ₀ , the SOC difference ΔSOC and the vehicle speed difference ΔV, and uses the APMP optimization algorithm to calculate the control speed of the engine and motor. Torque, start-stop status, clutch status, etc. are transmitted to the controllers of each executive part through the CAN bus to complete the vehicle control of the PHEV. The principle and optimization process of the APMP optimization module are as follows:

5.1 PMP Energy Management Strategy

The purpose of the present invention is to improve the fuel economy of the plug-in parallel hybrid electric vehicle under different working conditions, and make the plug-in parallel hybrid electric vehicle in a period of time (t ₀ ~ t _f seconds) The minimum fuel consumption is a typical global optimization problem, expressed by equations (2) and (3):

代表汽车在t时刻的瞬时燃油消耗速率(单位：kg/s)；f(x(t),u(t),t)是汽车在t时刻状态变量x(t)的变化率(单位：1/s)。因此式(2)为该全局优化问题的目标函数，表示汽车在t₀～t_f秒在特定工况运行过程中的总油耗，该优化问题的目标函数是要使得该段总油耗最小；式(3)包含两个式子均为该优化问题的约束，其中第一个式子表示该系统的状态转移方程，第二个式子表示该优化问题的终值约束条件，即当该过程结束时，车辆的SOC不能低于x_min。Among them: x(t) is the SOC of the vehicle at time t, that is, the state variable of the controlled system; u(t) is the torque distribution ratio at time t, that is, the system control variable, which is the motor torque T _m and the total demand ratio of torque T _dmd ;

Represents the instantaneous fuel consumption rate of the car at time t (unit: kg/s); f(x(t), u(t), t) is the rate of change of the state variable x(t) of the car at time t (unit: 1 /s). Therefore, Equation (2) is the objective function of the global optimization problem, which represents the total fuel consumption of the car during the operation of a specific working condition from t ₀ to t _f seconds. The objective function of this optimization problem is to minimize the total fuel consumption of this section; Equation (3) Contains two formulas that are the constraints of the optimization problem, where the first formula represents the state transition equation of the system, and the second formula represents the final value constraint of the optimization problem, that is, when the process ends When , the SOC of the vehicle cannot be lower than x _min .

The invention converts the global optimization problem of the formula (2) into several instantaneous optimization problems about the Hamiltonian function by using the Pontryagin extremum principle (PMP) by introducing cooperative state quantities. The PMP optimization algorithm defines the Hamiltonian function H(x(t), u(t), λ(t), t) as the optimization objective of the instantaneous optimization problem, as shown in formula (4):

Among them, λ(t) is the cooperative state quantity. The first item in the Hamilton function is the instantaneous fuel consumption of the engine, which can be found in the engine universal characteristic MAP diagram according to the engine speed and torque; the second item is the multiplication of the synergistic state value by the instantaneous change of SOC, and the synergistic state quantity is the PMP optimization algorithm The new state quantity introduced is related to the driving conditions. In different driving events, the initial value of the cooperative state quantity is different. Solving the minimum value of the Hamiltonian function at each moment, the optimal control variable sequence obtained is the optimization result, as shown in formula (5):

Its cooperative state transition equation is:

The influence of the power battery SOC on the instantaneous fuel consumption of the engine is not considered, so the partial derivative of the instantaneous fuel consumption of the engine to the SOC is 0.

The state transition equation of the system is:

Assuming that the rate of change of the battery SOC is approximately 0, solving the regular equation (7), it can be found that the change range of the cooperative state value is very small, which can be considered as a constant that does not change with time, that is,

In summary, the PMP optimization algorithm transforms the global optimization problem of minimizing fuel consumption into an instantaneous optimization problem of solving the minimum value of the Hamiltonian function. In the feasible region, solve the torque distribution ratio corresponding to the minimum value of the Hamiltonian function at all moments, which is the optimal control variable sequence. According to the above principle, the PMP energy management simulation model of plug-in hybrid electric vehicle is established. In this example, the PMP energy management strategy model is built by Matalb/Simulink, and the vehicle model is built by AVL Cruise. The above two models are connected for joint simulation to obtain the PHEV power By learning the joint simulation program, the fuel consumption value under a certain working condition can be obtained.

5.2 Initial Value Optimization of Collaborative State

The present invention uses a genetic algorithm to solve the initial value λ ₀ of the collaborative state in formula (9). The genetic algorithm takes the minimum fuel consumption as the optimization goal, and takes the initial value of the collaborative state λ ₀ as the individual, and the fitness function of the individual is:

The size of the synergy state value determines the oil-to-electricity distribution ratio of the entire operating condition, which is affected by two factors: the initial SOC of the power battery and the mileage. The Worldwide harmonized Light-duty driving Test Cycle (WLTC) is selected as the simulation condition, as shown in Figure 7. The WLTC working condition is divided into four stages: low-speed section, medium-speed section, high-speed section, and ultra-high-speed section. The average speeds range from low to high, respectively representing four typical driving conditions of branch road (Low3), main road (Medium3-1), suburban road (High3-1) and high speed (ExtraHigh3). The mileage of a single WLTC cycle is 23.2km, and the WLTC can be multiplied to obtain different mileage. When the SOC value is set to 0.35, it enters the battery maintenance mode. The initial SOC is 0.9, 0.8, 07, 0.6, 0.5, 0.4, and the simulation working conditions are 1 times, 2 times, 3 times, 4 times, 5 times the WLTC working conditions, and the synergy state values under a total of 30 conditions are solved.

Taking the initial SOC value of 0.9 and the driving condition of 5 times WLTC as an example, the following describes the process of genetic algorithm optimization of the synergy state value. Presetting the value range of the cooperative state value firstly can speed up the optimization speed by reducing the value range of the variables in the genetic algorithm. In this example, the GA toolbox of Matlab is used to solve the problem, the variable range is set to [-1,-4], and the maximum genetic algebra is 15. The GA toolbox calls the PHEV dynamics co-simulation program to calculate the fitness function, and the minimum value of the fitness function corresponds to The synergy state value is the optimization result, which is -1.94kg in this example. The coordinated state values solved under 30 working conditions are shown in Table 1, and the coordinated state value MAP diagram is drawn, as shown in Figure 8. Before the start of the trip, the system determines the initial value of the system state λ ₀ through the initial value of the current SOC and the total mileage of the future trip, using the cooperative state value MAP graph interpolation.

Table 1 Oil-electric equivalent factor MAP data (unit: -1×kg)

5.3 Cooperative state value online correction strategy

The cooperative state value obtained through genetic algorithm optimization above is the optimal value under the working condition WLTC. However, the actual travel conditions are complex and changeable, so in order to realize the condition adaptive control strategy on the real vehicle, it is necessary to further correct the cooperative state value λ ₀ in real time. The present invention is based on the reference SOC _ref obtained in step 4, and introduces the SOC penalty factor and the speed penalty factor, so that the actual SOC can follow the reference SOC _ref in real time, and the corrected cooperative state value is calculated by the following formula:

λ(t)=λ ₀ +s(ΔSOC,t)+s(ΔV,t) (11)

The APMP optimization algorithm can realize the self-adaptation of working conditions, in which the cooperative state value λ(t) plays a key role, and the value of λ(t) determines the proportion of gasoline and electricity used. When the value of λ(t) is too large, the control strategy tends to use more fuel (engine), and when the value of λ(t) is too small, the control strategy tends to use more electricity (motor). Therefore, λ(t) can adjust the torque distribution ratio of the engine and the motor. λ(t) is affected by two factors: SOC difference (ΔSOC=SOC-SOC _ref ) and vehicle speed difference (ΔV=VV _m ). When the vehicle is driving on a relatively congested road section and the front speed will be lower than the average speed of the entire journey, that is, the value of ΔV=VV _m is negative, the value of λ(t) can be reduced accordingly, so that the system tends to use more motors, and the low-speed area Driving with an electric motor is good for fuel economy and vice versa. Due to changes in working conditions, the actual SOC drop curve will not completely follow the reference SOC, as shown in Figure 9. The λ(t) value is also affected by the SOC difference. When the driving distance is S1, its ΔSOC is a negative value, and the actual SOC is smaller than the reference SOC. In order to achieve the SOC following effect and reduce power consumption, it can be increased accordingly The value of λ(t) makes the system bias towards using the engine more, and vice versa.

The penalty factor s(ΔSOC,t) of the SOC difference takes a minimum value when the deviation from the reference SOC is small, and the value should increase rapidly when the deviation from the reference SOC is too large. When ΔSOC>0, in order to speed up the use of power, the value of the penalty factor s is positive. When ΔSOC<0, in order to slow down the power consumption, the value of the penalty factor s is negative. Therefore, the penalty factor s(ΔSOC,t) is expressed as follows:

Set the value range of ΔSOC to be (-0.1, 0.1), and the penalty factor curve of s(ΔSOC,t) is shown in Figure 10.

The penalty factor s(ΔV,t) of the vehicle speed difference should take a minimum value when the deviation from the average vehicle speed is small, and the value should increase rapidly when the deviation from the average vehicle speed is too large. When ΔV>0, in order to use electricity slowly, the value of penalty factor s is negative. When ΔV<0, in order to speed up the use of power, the value of the penalty factor s is positive. Therefore, the penalty factor s(ΔV,t) is expressed as follows:

Set the value range of ΔV to (-10, 10), and the penalty factor of s(ΔV,t) is shown in Figure 11.

5.4 Solution of Hamilton function

Since the Hamiltonian function is a very complicated functional equation to solve, the present invention uses a numerical solution to solve the Hamiltonian function. The solution process of the Hamiltonian function is shown in Figure 12. First, the dynamic equation is calculated to obtain the total demand torque, and then the initial value of the cooperative state is determined by interpolation method according to the travel mileage of this trip and the initial SOC, and the SOC difference and SOC are determined by the vehicle state. Vehicle speed difference, correct the current coordination state value. Then the Hamiltonian function is established, and the feasible region of the torque distribution ratio u(t) is divided into 100 parts. If u(t)>0, it means that the motor and the engine jointly drive the vehicle or the motor drives the vehicle alone. If u(t)<0, it means the motor is in generator state. The numerical grid division method can guarantee the solution accuracy in the feasible region. Calculate all the Hamilton function values after division, and find the minimum torque distribution ratio of the corresponding Hamilton function.

Claims (6)

1. A PHEV adaptive optimal energy management method based on path information, is characterized in that, comprises the following steps: Step 1. Driving conditions and mileage prediction: 1.1) Obtain the vehicle location information through the vehicle navigation system and plan the driving route in the electronic map, and at the same time obtain the real-time road condition information of the planned driving route, and generate the predicted working conditions of the ahead route; 1.2) The car navigation system obtains the historical driving data of the vehicle, establishes a travel mileage prediction strategy to predict the daily travel mileage of users, and draws the cumulative average mileage curve of the vehicle; Step 2. Generate a reference SOC based on the SOC planning algorithm: Through the forecast data generated in step 1, including the forecast working conditions, the total mileage of the whole day, the mileage of this trip and the initial SOC, the reference SOC is generated based on the SOC planning algorithm; Step 3, APMP optimization algorithm: 3.1) With the minimum fuel consumption as the global optimization goal, the collaborative state value is introduced, and the global optimization problem is transformed into several instantaneous optimization problems with Hamiltonian operators; 3.2) Optimization of the initial value of the collaborative state: the initial value of the collaborative state is optimized using a genetic algorithm, and a MAP diagram of the initial value of the collaborative state changing with the initial value of the SOC and the travel mileage is established; 3.3) On-line correction of the cooperative state value: Under actual working conditions, the initial value of the cooperative state is obtained in the MAP diagram by interpolation method, and the initial value of the cooperative state is corrected in real time according to the working condition information obtained by the vehicle navigation system and the reference SOC; The online correction of the coordination state value includes the following process: According to the reference SOC obtained in step 2, and introduce the SOC penalty factor and the speed penalty factor, so that the actual SOC can follow the reference SOC in real time, and the corrected cooperative state value is calculated by the following formula: λ(t)=λ ₀ +s(ΔSOC,t)+s(ΔV,t) λ(t) is affected by two factors: SOC difference ΔSOC and vehicle speed difference ΔV, ΔSOC=SOC-SOC _ref , ΔV=VV _m ; The penalty factor s(ΔSOC,t) of the SOC difference takes a minimum value when the deviation from the reference SOC is small, and the value should increase rapidly when the deviation from the reference SOC is too large; When ΔSOC>0, the value of penalty factor s is positive; when ΔSOC<0, the value of penalty factor s is negative; the expression of penalty factor s(ΔSOC,t) is as follows:

The penalty factor s(ΔV,t) of the vehicle speed difference takes a minimum value when the deviation from the average vehicle speed is small, and the value should increase rapidly when the deviation from the average vehicle speed is too large; When ΔV>0, the value of penalty factor s is negative; when ΔV<0, the value of penalty factor s is positive; the expression of penalty factor s(ΔV,t) is as follows:

3.4) Solve the Hamilton function, use the PMP optimization algorithm to distribute the power, and transmit it to the controllers of each executive component through the CAN bus to complete the vehicle control of the PHEV. 2. A PHEV adaptive optimal energy management method based on path information as claimed in claim 1, characterized in that, the specific process of said step 1.2) is: the PHEV energy management system calculates the user's daily vehicle speed-time mileage The data is recorded, and then the vehicle speed is integrated to obtain the driving mileage, and the driving mileage of the day is counted by hour, and written into the travel mileage characteristic database. The driving mileage on weekdays and holidays needs to be counted separately; Mileage, draw the cumulative average mileage curve of each time period on weekdays and holidays. 3. A kind of PHEV adaptive optimal energy management method based on path information as claimed in claim 1, is characterized in that, the specific process of described step 2 is: First generate the reference SOC: take the mileage as the abscissa and the SOC as the ordinate, connect the two points (0,SOC _ini ) and (S _t ,SOC _min ) to get the SOC _ref ; Among them, SOC _ref is the reference SOC; SOC _ini is the initial SOC of the trip; SOC _min is the minimum SOC of the CD mode; S _t is the total travel mileage of the whole day, and S _{t is} the cumulative average mileage of the vehicle obtained from the step 1 according to the current travel time The interpolation in the curve is obtained; The system will carry out SOC planning before each trip. For a single trip, the calculation formula of SOC _end at the end of this trip is as follows:

Among them, S _i is the mileage of this trip. 4. A kind of PHEV self-adaptive optimal energy management method based on route information as claimed in claim 1, it is characterized in that, described step 3.1) take minimum fuel consumption as global optimization goal, introduce cooperative state value, global optimization problem The specific process of transforming into several instantaneous optimization problems with Hamiltonian operator is: Taking the minimum fuel consumption as the global optimization problem, the objective function is:

Among them: x(t) is the SOC of the vehicle at time t, that is, the state variable of the controlled system; u(t) is the torque distribution ratio at time t, that is, the system control variable, which is the motor torque T _m and the total demand ratio of torque T _dmd ;

Represents the instantaneous fuel consumption rate of the car at time t, unit: kg/s; The constraints of this optimization problem include:

Among them, f(x(t), u(t), t) is the rate of change of the state variable x(t) of the car at time t, unit: 1/s; Among them, the first formula represents the state transition equation of the system, and the second formula represents the final value constraint condition of the optimization problem, that is, when the process ends, the SOC of the vehicle cannot be lower than x _min ; Using Pontryagin's extreme value principle to convert the global optimization problem of formula (2) into several instantaneous optimization problems about the Hamiltonian function by introducing cooperative state quantities: PMP optimization algorithm defines the Hamiltonian function H(x(t),u(t) ,λ(t),t) as the optimization objective of the instantaneous optimization problem, as shown in the following formula:

Among them, λ(t) is the cooperative state quantity; the first item in the Hamiltonian function is the instantaneous fuel consumption of the engine, which is found in the engine universal characteristic MAP diagram according to the engine speed and torque; the second item is the multiplication of the cooperative state value by the SOC Instantaneous change, the cooperative state quantity is a new state quantity introduced by the PMP optimization algorithm, which is related to the driving conditions. In different driving events, the initial value of the cooperative state quantity is different; the minimum value of the Hamilton function at each moment is solved, The obtained optimal control variable sequence is the optimization result, as shown in the following formula:

Its cooperative state transition equation is:

5. A kind of PHEV self-adaptive optimal energy management method based on path information as claimed in claim 1, is characterized in that, described step 3.2) optimization of cooperative state initial value comprises the following process: The genetic algorithm is used to solve the initial value of the cooperative state λ ₀ , The genetic algorithm takes the minimum fuel consumption as the optimization goal, and takes the initial value of the collaborative state λ ₀ as the individual, and the fitness function of the individual is:

The synergy state value corresponding to the minimum value of the fitness function is the optimization result, and a synergy state value MAP diagram is drawn; before the start of the trip, the system uses the synergy state value MAP diagram interpolation to determine the system State initial value λ ₀ . 6. A kind of PHEV self-adaptive optimal energy management method based on path information as claimed in claim 1, is characterized in that, described step 3.4) comprises the following process to Hamilton function solution: Use the numerical solution to solve the Hamiltonian function: first calculate the dynamic equation to obtain the total demand torque, then determine the initial value of the cooperative state by interpolation method according to the travel mileage of this trip and the initial SOC, and determine the SOC difference and vehicle speed difference based on the vehicle state , modify the current cooperative state value; establish a Hamiltonian function to divide the feasible region of the torque distribution ratio u(t) into several parts; if u(t)>0, it means that the motor and the engine drive the vehicle together or the motor drives the vehicle alone; If u(t)<0, it means that the motor is in the generator state; calculate all the Hamiltonian function values after numerical grid division, and find the minimum torque distribution ratio of the corresponding Hamiltonian function.

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