CN113060120A - Intelligent hybrid electric vehicle self-adaptive energy management method - Google Patents

Intelligent hybrid electric vehicle self-adaptive energy management method Download PDF

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CN113060120A
CN113060120A CN202110229075.7A CN202110229075A CN113060120A CN 113060120 A CN113060120 A CN 113060120A CN 202110229075 A CN202110229075 A CN 202110229075A CN 113060120 A CN113060120 A CN 113060120A
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CN113060120B (en
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张风奇
胡晓松
肖乐华
王利花
崔亚辉
庞辉
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Xian University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0037—Mathematical models of vehicle sub-units
    • B60W2050/0039—Mathematical models of vehicle sub-units of the propulsion unit
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0037—Mathematical models of vehicle sub-units
    • B60W2050/0041—Mathematical models of vehicle sub-units of the drive line

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Abstract

The invention discloses an intelligent hybrid electric vehicle self-adaptive energy management method, which is implemented according to the following steps: establishing a parallel A-HEVs model of the single-shaft parallel hybrid electric vehicle; an ECMS is adopted to optimize gear shifting commands and torque distribution, and an optimized gear shifting diagram is extracted by combining a parallel A-HEVs model; developing an improved ECMS with flexible torque requirements and battery life considerations by optimizing the shift map; designing the self-adaptive law of main parameters in the objective function of the improved ECMS to realize the self-adaptive energy management of the intelligent hybrid electric vehicle; the management method comprehensively considers drivability and fuel economy, and adopts ECMS to cooperatively optimize gear shifting decision and torque distribution so as to extract an optimized gear shifting diagram, so that the gear shifting frequency is greatly reduced; the improved ECMS is formed by introducing flexible torque requirements and considering the service life of a battery, the torque is optimally distributed, and the fuel efficiency and the service life of the battery are improved while the traffic efficiency is ensured.

Description

Intelligent hybrid electric vehicle self-adaptive energy management method
Technical Field
The invention belongs to the technical field of new energy automobile power design, and particularly relates to an intelligent hybrid electric vehicle self-adaptive energy management method.
Background
Automobile electrification and intellectualization are becoming important development trends of sustainable traffic in the future. In recent years, smart hybrid vehicles (a-HEVs) have received increasing attention due to their excellent energy efficiency and autonomy, as compared to conventional vehicles. In view of considering the power requirements of vehicles, a major challenge in designing a-HEVs is how to distribute power among multiple energy sources. The reduction in emissions and improvement in fuel efficiency of A-HEVs is largely dependent on Energy Management Strategies (EMS). To improve fuel efficiency, the EMS of A-HEVs can be generally implemented with a two-tier framework. The upper layer controls the overall kinematics of the vehicle, and the fuel efficiency is improved by changing the vehicle speed on the premise of meeting the vehicle safety and road constraints. The lower layer is optimized for power distribution among different power sources, and the power distribution is realized to meet the power requirement of the upper layer. Due to the complex and trivial design process, it is very challenging to design the energy management strategy to fully realize the fuel saving potential. For this reason, EMS design for A-HEVs is increasingly being carried out using optimization-based methods.
Dynamic Programming (DP), Pointryagin Minimum Principle (PMP), Model Predictive Control (MPC), and Equivalent Cost Minimization Strategy (ECMS) are commonly used to develop EMS for hybrid vehicles. DP can achieve global optimality, but it cannot be applied directly to vehicles in practice because it requires a priori knowledge of the overall driving conditions (speed, road slope, etc.) and is computationally expensive. The ECMS can realize real-time optimization, and the performance of the ECMS is closely related to the equivalence factor. PMP converts the global optimization problem into an instantaneous hamilton optimization problem. The power allocation is optimized by minimizing the instantaneous hamiltonian. The MPC obtains optimal control by optimizing a rolling time domain objective function, and considers uncertainty of future working conditions. Most researches aim at the economic driving and energy management optimization of the intelligent HEV in a following scene, and the energy management optimization of the intelligent HEV in a single-vehicle driving scene is less involved.
AMT equipped parallel hybrid vehicles are very popular in the market, especially for commercial vehicles such as trucks, because they can meet the requirements for fuel economy, emissions and purchase cost. Their energy management includes torque distribution and shift commands. Because fuel economy and drivability are tradeoffs, it is very challenging to cooperatively optimize torque distribution and shift commands. Drivability is primarily a consideration of whether frequent shifts are used as evaluation criteria (e.g., frequent upshifts and downshifts within short time intervals). ECMS is realized by minimizing instantaneous fuel consumption, the whole driving condition is not required to be known in advance, and the method is very suitable for real-time optimization of power assembly control. But this may lead to frequent shifts. In order to reduce the calculation cost, a William model is adopted to simplify an engine model and a motor model, and ECMS is used for optimizing gear selection and torque distribution. Therefore, it is very important how to optimize the shift decisions and torque distribution in coordination, considering fuel economy and drivability.
In summary, the dynamic characteristics of the hybrid powertrain are not fully considered in the optimization process, so that the degree of freedom of control is reduced, which limits further improvement of fuel efficiency. On the other hand, due to low calculation efficiency and the requirement of priori knowledge of the driving conditions, real-time application is difficult. Furthermore, most of the research only relates to energy distribution of the intelligent power split hybrid electric vehicle, and does not relate to gear shifting decision. Integrating discrete shift decisions with continuous torque split optimization problems is computationally more demanding and challenging.
Disclosure of Invention
The invention aims to provide an intelligent hybrid electric vehicle self-adaptive energy management method, which improves the fuel efficiency and prolongs the service life of a battery while ensuring the traffic efficiency.
The technical scheme adopted by the invention is that the intelligent hybrid electric vehicle self-adaptive energy management method is implemented according to the following steps:
step 1, establishing a parallel A-HEVs model of a single-shaft parallel hybrid electric vehicle;
step 2, optimizing a gear shifting command and torque distribution by adopting an ECMS (electric control mechanical systems), and combining a parallel A-HEVs (automatic gearbox control systems) model to extract an optimized gear shifting diagram;
step 3, developing an improved ECMS with flexible torque requirements and considering battery life by optimizing a gear shifting diagram;
and 4, designing the self-adaptive law of the main parameters in the objective function of the improved ECMS to realize the self-adaptive energy management of the intelligent hybrid electric vehicle.
The invention is also characterized in that:
the specific process of the step 1 is as follows: respectively establishing mathematical models for an engine, a motor, a battery, a transmission and vehicle dynamics of the single-shaft parallel hybrid electric vehicle;
(1) establishing a mathematical model of engine efficiency and engine speed and engine output torque for the engine:
the engine efficiency is determined by the engine speed and the engine output torque, as shown in equation (1);
ηe＝f(ne,Te) (1)
the engine output torque is calculated by equation (2);
Te＝αTemax(ne) (2)
fitting a curve of the maximum torque of the engine by the formula (3), and obtaining a fitting coefficient by an MATLAB curve fitting tool;
Temax＝k0+k1cos(ne·c)+p1sin(ne·c)+k2cos(2ne·c)+p2sin(2ne·c) (3)
wherein n iseIs the engine speed, TeIs the engine output torque, ηeIs the engine efficiency, alpha is the engine throttle opening, Temax(ne) Is the maximum torque of the engine at the current speed, c, k0，k1，k2，p1，p2Is a fitting coefficient;
(2) establishing a relation model of battery power and motor rotating speed:
according to the functional relation among the motor efficiency, the motor rotating speed and the motor torque, the method comprises the following steps:
ηm＝ψ(nm,Tm) (4)
the relation model of the battery power and the motor rotating speed is expressed as;
Figure BDA0002958199600000041
wherein n ismIndicating motor speed, TmRepresenting motor torque, ηmIndicating motor efficiency, PbRepresents the power required by the battery;
(3) establishing a state of charge dynamic equation for the battery:
Figure BDA0002958199600000042
wherein R isinIs the internal resistance of the battery, VocIs an open circuit voltage, QmaxIs the maximum capacity;
(4) modeling torque and speed for the transmission:
the rotating parts are assumed to be rigid and expressed in concentrated mass, and the torque expression of the transmission is as follows:
Figure BDA0002958199600000043
the speed expression of the transmission is:
win＝woutig(Gear)i0 (8)
wherein, ToutRepresenting the output torque, T, of the transmissioninRepresenting transmission input shaft torque, ηGRRepresenting transmission efficiency, ig(Gear) denotes each transmission Gear ratio, i0Representing the main reducer transmission ratio, Gear representing the number of gears, winRepresenting angular speed, w, of the input shaft of the transmissionoutRepresenting transmission output shaft angular speed;
(5) under the requirements of the fixed torque of the transmission and the flexible torque of the transmission, a vehicle dynamic model is established:
Figure BDA0002958199600000051
Figure BDA0002958199600000052
nin(t)＝30igi0va(t)/πr (11)
wherein,
Figure BDA0002958199600000053
indicating a compliant torque request, T, on the transmission input shaftdem(t) represents a fixed torque demand on the transmission input shaft, CDRepresenting the coefficient of air resistance, A representing the frontal area, va(t) represents the longitudinal vehicle speed generated by the upper level controller,
Figure BDA0002958199600000054
representing variable longitudinal vehicle speed, m representing vehicle trim mass, f representing rolling resistance coefficient, delta representing rotating mass coefficient, r representing wheel radius, ninRepresenting the speed of the transmission input shaft, FbrakeIndicating the braking force.
The specific process of the step 2 is as follows:
step 2.1, establishing an objective function of the gear shifting diagram:
Figure BDA0002958199600000055
wherein s (t) is an equivalent factor, Pb(ug(t)) represents the battery power, QLHVRepresents a lower heating value of the fuel;
step 2.2, solving the optimized torque distribution and the optimized gear shifting command;
(1) optimizing the distribution process of the output torque of the engine and the torque of the motor, wherein the torque of the input shaft of the transmission meets the constraint conditions as follows:
Figure BDA0002958199600000056
Tdem＝Tv/ig(Gear)i0. (14)
in the formulae (14) and (15), TdemRepresenting transmission input shaft torque, TvRepresenting torque at the wheels;
(2) the optimized gear shifting command sh (t) comprises the following processes:
the optimal gear is obtained by equation (16), and is constrained by equation (17):
g(t)＝g(t-1)+sh(t),sh(t)∈{-1,0,1} (15)
1≤g(t)≤5. (16)
wherein, the numerical value { -1,0,1} respectively represents a downshift, a constant gear and an upshift;
step 2.3, obtaining the optimal control input u according to the optimized gear shifting command and the output torque of the engine under the constraint condition of meeting the torque of the input shaft of the transmissiong(t)，
ug(t)＝[Te(t),sh(t)] (18)；
Step 2.4, inputting u according to the optimal controlg(t) and equation (12), obtaining an objective function of the optimized shift diagram and a constraint condition satisfied by the objective function:
the objective function is:
uopt(t)＝min{L[ug(t),s(t)]} (17)
the constraint is expressed as:
Figure BDA0002958199600000061
wherein, Tm(T) represents motor torque, Te(t) represents engine output torque; t ism_min(nm(T)) represents the minimum torque of the motor at the current speed, Tm_max(nm(t)) represents the motor torque capacity at the current speed; t ise_max(ne(t)) represents the current speedLower engine torque capacity; n ism_maxRepresenting the maximum speed of the motor, ne_minRepresenting minimum engine speed, ne_maxRepresenting a maximum engine speed; SOCminIndicating minimum state of charge, SOCmaxIndicating the maximum state of charge.
The specific process of the step 3 is as follows: the battery capacity loss Q during battery aging is calculated by adopting a cyclic semi-empirical battery aging modelloss：
Figure BDA0002958199600000062
Wherein QlossIs the battery capacity loss, α, β and η are fitting coefficients related to SOC, EaIs an activation energy, RgIs the gas constant, T is the cell temperature, Ah is the cumulative charge throughput, IrateIs the current velocity, z is the power law factor;
wherein,
Figure BDA0002958199600000071
Figure BDA0002958199600000072
wherein, Ib(t) represents current, Q represents maximum charge capacity;
under the nominal condition of Irate,norm＝2.5[1/h]，SOCnorm0.35 and TnormThe nominal battery life is calculated (273.15+25) K:
Figure BDA0002958199600000073
the actual battery life is defined in terms of Ah throughput associated with a particular operating condition, and is expressed as:
Figure BDA0002958199600000074
and calculating the aging intensity coefficient of the battery according to the nominal service life and the actual service life of the battery:
Figure BDA0002958199600000075
the objective function of the improved ECMS is designed based on the above parameters as:
Figure BDA0002958199600000076
wherein β, γ are coefficients, and u (T) ═ Te_opt,Tm_optK is a weighting factor for the aging cost of the battery, CaIs the ratio of the replacement cost of the battery to the price of 1 kilogram of gasoline;
minimizing the objective function of the instantaneous modified ECMS yields:
[Te_opt,Tm_opt]＝min{L[u(t)]} (26)
by obtaining the amount of change in the vehicle speed by equations (9) and (10), the vehicle dynamics model is represented by equation (29):
Figure BDA0002958199600000081
the amounts of change in the vehicle speed, the travel distance, and the transmission torque request are obtained by equations (30) to (32):
Figure BDA0002958199600000082
Figure BDA0002958199600000083
Figure BDA0002958199600000084
further obtaining:
Figure BDA0002958199600000085
assuming that the vehicle with the compliant torque request and the fixed torque request should reach the same position at the end of the operating condition, the relationship between Δ x and Δ v is expressed as:
Figure BDA0002958199600000086
and determining the constraint conditions of the flexible torque requirement, namely the constraint conditions of the improved ECMS are as follows:
Figure BDA0002958199600000087
the amount of change in compliance torque is expressed as:
Figure BDA0002958199600000088
limited by the maximum torque of the engine and the electric machine at the current speed;
where b, b are the lower and upper limits, T, respectively, of the flexible transmission torque requeste_opt，Tm_optEngine output torque and motor torque, respectively, for the flexible torque demand strategy.
The specific process of the step 4 is as follows:
using the concept of Proportional Integral (PI) controller to adjust the coefficients β, γ, there are:
Figure BDA0002958199600000089
Figure BDA0002958199600000091
where β (k) is a torque coefficient at time k, γ (k) is a speed coefficient at time k (k is 1,2,3 …), and β (k) is a torque coefficient at time k0，γ0Is the initial value of the time-domain clock,
Figure BDA0002958199600000092
is the optimum compliant torque requirement, TdemIs the fixed torque demand generated from the upper speed controller,
Figure BDA0002958199600000093
and x is the distance traveled for the soft and fixed torque requirements, Kp，KiIs a coefficient;
and (3) substituting the expressions (37) and (38) into the expression (28) to obtain the corresponding engine output torque and the corresponding motor torque, namely realizing the self-adaptive energy management of the intelligent hybrid electric vehicle.
The invention has the beneficial effects that:
(1) the invention comprehensively considers drivability and fuel economy, and adopts ECMS to cooperatively optimize gear shifting decision and torque distribution to extract an optimized gear shifting diagram, thereby greatly reducing frequent gear shifting and improving driving comfort.
(2) The invention introduces the idea of flexible torque demand and battery life consideration into the ECMS to form an improved ECMS, optimally distributes the torque, and improves the fuel efficiency and the battery life while ensuring the traffic efficiency.
(3) According to the invention, the self-adaptive law of the main parameters in the objective function is designed, so that the algorithm has better control performance and adaptability to different driving conditions.
Drawings
FIG. 1 is a topology of a parallel hybrid vehicle powertrain for use in the present invention;
FIG. 2 is a flow chart of an intelligent hybrid electric vehicle adaptive energy management method of the present invention;
FIG. 3 is a flow chart of a coordinated optimization of shift commands and torque distribution taking into account compliance torque demand and battery life.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a self-adaptive energy management method of an intelligent hybrid electric vehicle, which is implemented according to the following steps as shown in figure 2:
step 1, adopting a single-shaft parallel hybrid electric vehicle, wherein a topological diagram of the single-shaft parallel hybrid electric vehicle is shown in figure 1. The motor is positioned between the clutch and an Automatic Mechanical Transmission (AMT), and can realize a plurality of working modes; establishing a parallel A-HEVs model of the single-shaft parallel hybrid electric vehicle; the specific process of the step 1 is as follows: respectively establishing mathematical models for an engine, a motor, a battery, a transmission and vehicle dynamics of the single-shaft parallel hybrid electric vehicle;
(1) establishing a mathematical model of engine efficiency and engine speed and engine output torque for the engine:
the engine efficiency is determined by the engine speed and the engine output torque, as shown in equation (1);
ηe＝f(ne,Te) (37)
the engine output torque is calculated by equation (2);
Te＝αTemax(ne) (38)
fitting a curve of the maximum torque of the engine by the formula (3), and obtaining a fitting coefficient by an MATLAB curve fitting tool;
Temax＝k0+k1cos(ne·c)+p1sin(ne·c)+k2cos(2ne·c)+p2sin(2ne·c) (39)
wherein n iseIs the engine speed, TeIs the engine output torque, ηeIs the engine efficiency, alpha is the engine throttle opening, Temax(ne) Is the maximum torque of the engine at the current speed, c, k0，k1，k2，p1，p2Is a fitting coefficient;
(2) establishing a relation model of battery power and motor rotating speed:
according to the functional relation among the motor efficiency, the motor rotating speed and the motor torque, the method comprises the following steps:
ηm＝ψ(nm,Tm) (40)
the relation model of the battery power and the motor rotating speed is expressed as;
Figure BDA0002958199600000111
wherein n ismIndicating motor speed, TmRepresenting motor torque, ηmIndicating motor efficiency, PbRepresents the power required by the battery;
(3) the battery model is typically complex, being affected by internal resistance, temperature, open circuit voltage, and state of charge (SOC). The battery internal resistance model is often used to design a full-vehicle-level EMS. Neglecting the influence of the loss, temperature and service life of the battery on the performance of the battery; establishing a state of charge dynamic equation for the battery:
Figure BDA0002958199600000112
wherein R isinIs the internal resistance of the battery, VocIs an open circuit voltage, QmaxIs the maximum capacity;
(4) for transmission modeling, the rotating components in the drive train are typically considered to be rigid, represented by a concentrated mass. Additionally, the torsional and lateral vibrations of each rotating component are ignored, and the dynamic characteristics are not considered. Modeling torque and speed for the transmission:
the rotating parts are assumed to be rigid and expressed in concentrated mass, and the torque expression of the transmission is as follows:
Figure BDA0002958199600000113
the speed expression of the transmission is:
win＝woutig(Gear)i0 (44)
wherein, ToutRepresenting the output torque, T, of the transmissioninRepresenting transmission input shaft torque, ηGRRepresenting transmission efficiency, ig(Gear) denotes each transmission Gear ratio, i0Representing the main reducer transmission ratio, Gear representing the number of gears, winRepresenting angular speed, w, of the input shaft of the transmissionoutRepresenting transmission output shaft angular speed;
(5) assuming that the vehicle is traveling on a flat road, without grade, only longitudinal vehicle dynamics are considered. Since the amount of change is added to the fixed required torque, the vehicle speed changes accordingly. The vehicle dynamics model is represented by equations (9) and (10) at fixed and flexible torque demands. Hereinafter, the "to" are taken to mean variables corresponding to the flexible torque demand strategy. The speed of the input shaft of the gearbox can be expressed by an equation (11), and a vehicle dynamic model is established:
Figure BDA0002958199600000121
Figure BDA0002958199600000122
nin(t)＝30igi0va(t)/πr (47)
wherein,
Figure BDA0002958199600000123
indicating a compliant torque request, T, on the transmission input shaftdem(t) represents a fixed torque demand on the transmission input shaft, CDRepresenting the coefficient of air resistance, A representing the frontal area, va(t) represents the longitudinal vehicle speed generated by the upper level controller,
Figure BDA0002958199600000126
representing variable longitudinal vehicle speed, m representing vehicle trim mass, f representing rolling resistance coefficient, and δ representing rotationMass coefficient, r represents wheel radius, ninRepresenting the speed of the transmission input shaft, FbrakeIndicating the braking force.
Step 2, optimizing a gear shifting command and torque distribution by adopting an ECMS (electric control mechanical systems), and combining a parallel A-HEVs (automatic gearbox control systems) model to extract an optimized gear shifting diagram; the specific process of the step 2 is as follows:
step 2.1, establishing an objective function of the gear shifting diagram:
Figure BDA0002958199600000124
wherein s (t) is an equivalent factor, Pb(ug(t)) represents the battery power, QLHVRepresents a lower heating value of the fuel;
step 2.2, solving the optimized torque distribution and the optimized gear shifting command;
(1) optimizing the distribution process of the output torque of the engine and the torque of the motor, wherein the torque of the input shaft of the transmission meets the constraint conditions as follows:
Figure BDA0002958199600000125
Tdem＝Tv/ig(Gear)i0. (50)
in the formulae (14) and (15), TdemRepresenting transmission input shaft torque, TvRepresenting torque at the wheels;
(2) the optimized gear shifting command sh (t) comprises the following processes:
as previously mentioned, the optimal control inputs include shift commands and torque distribution. The shift command sh (t) is therefore used to determine the optimum gear, which can be obtained from equation (16). The constraint condition is shown as formula (17);
g(t)＝g(t-1)+sh(t),sh(t)∈{-1,0,1} (51)
1≤g(t)≤5. (52)
wherein, the numerical value { -1,0,1} respectively represents a downshift, a constant gear and an upshift;
step 2.3, satisfying the constraint of the transmission input shaft torqueUnder the condition, obtaining the optimal control input u according to the optimized gear shifting command and the output torque of the engineg(t)，
ug(t)＝[Te(t),sh(t)] (18)；
Step 2.4, inputting u according to the optimal controlg(t) and equation (12), obtaining an objective function of the optimized shift diagram and a constraint condition satisfied by the objective function:
the objective function is:
uopt(t)＝min{L[ug(t),s(t)]} (53)
the constraint is expressed as:
Figure BDA0002958199600000131
wherein, Tm(T) represents motor torque, Te(t) represents engine output torque; t ism_min(nm(T)) represents the minimum torque of the motor at the current speed, Tm_max(nm(t)) represents the motor torque capacity at the current speed; t ise_max(ne(t)) represents the engine torque capacity at the current speed; n ism_maxRepresenting the maximum speed of the motor, ne_minRepresenting minimum engine speed, ne_maxRepresenting a maximum engine speed; SOCminIndicating minimum state of charge, SOCmaxIndicating the maximum state of charge.
Step 3, developing an improved ECMS with flexible torque requirements and considering battery life by optimizing a gear shifting diagram; the specific process of the step 3 is as follows: in the invention, by considering the flexible torque requirement and the battery life, the part of the A-HEVs self-adaptive energy management strategy constructed based on the improved ECMS is mainly as follows: a multi-objective optimization problem is constructed by taking into account battery aging. Generally, a battery is a complex electrochemical system. The invention considers the control-oriented battery cycle aging model when optimizing the power distribution and adopts the cycle semi-empirical battery aging model. The battery pack adopts a LiFePO4 battery, and the battery capacity loss Q during battery aging is calculatedloss：
Figure BDA0002958199600000141
Wherein QlossIs the battery capacity loss, α, β and η are fitting coefficients related to SOC, EaIs an activation energy, RgIs the gas constant, T is the cell temperature, Ah is the cumulative charge throughput, IrateIs the current velocity, z is the power law factor;
wherein,
Figure BDA0002958199600000142
Figure BDA0002958199600000143
wherein, Ib(t) represents current, Q represents maximum charge capacity;
for the HEV, it is found that the battery should be replaced when the battery capacity reaches 80% of the original value. Battery end-of-life is therefore defined as 20% capacity loss. Under the nominal condition of Irate,norm＝2.5[1/h]，SOCnorm0.35 and TnormThe nominal battery life is calculated (273.15+25) K:
Figure BDA0002958199600000144
the actual battery life is defined in terms of Ah throughput associated with a particular operating condition, and is expressed as:
Figure BDA0002958199600000151
and calculating the aging intensity coefficient of the battery according to the nominal service life and the actual service life of the battery:
Figure BDA0002958199600000152
FIG. 3 is a flow chart of a coordinated optimization of shift commands and torque distribution taking into account compliance torque demand and battery life. The extracted shift pattern is applied to the ECMS, and due to the fact that a flexible torque requirement is introduced, the speed of the vehicle can be adjusted, and therefore traffic efficiency is affected. To ensure traffic efficiency while taking battery life into account, the improved objective function based on ECMS is shown as equation (27):
Figure BDA0002958199600000153
wherein β, γ are coefficients, and u (T) ═ Te_opt,Tm_optK is a weighting factor for the aging cost of the battery, CaIs the ratio of the replacement cost of the battery to the price of 1 kilogram of gasoline;
minimizing the objective function of the instantaneous modified ECMS yields:
[Te_opt,Tm_opt]＝min{L[u(t)]} (62)
by obtaining the amount of change in the vehicle speed by equations (9) and (10), the vehicle dynamics model is represented by equation (29):
Figure BDA0002958199600000154
the amounts of change in the vehicle speed, the travel distance, and the transmission torque request are obtained by equations (30) to (32):
Figure BDA0002958199600000155
Figure BDA0002958199600000156
Figure BDA0002958199600000157
further obtaining:
Figure BDA0002958199600000161
assuming that the vehicle with the compliant torque request and the fixed torque request should reach the same position at the end of the operating condition, the relationship between Δ x and Δ v is expressed as:
Figure BDA0002958199600000162
and determining the constraint conditions of the flexible torque requirement, namely the constraint conditions of the improved ECMS are as follows:
Figure BDA0002958199600000163
the amount of change in compliance torque is expressed as:
Figure BDA0002958199600000164
limited by the maximum torque of the engine and the electric machine at the current speed;
where b, b are the lower and upper limits, T, respectively, of the flexible transmission torque requeste_opt，Tm_optEngine output torque and motor torque, respectively, for the flexible torque demand strategy.
Step 4, designing the self-adaptive law of main parameters in the objective function of the improved ECMS to realize the self-adaptive energy management of the intelligent hybrid electric vehicle;
the specific process of the step 4 is as follows:
in the improved ECMS, the main parameters (e.g., β, γ) have a significant impact on the performance of the EMS, especially on the travel distance variation (Δ x). Better control performance can be obtained if appropriate β, γ is selected. Generally, the weight coefficients β, γ are selected mainly considering two aspects:
(1) by choosing the appropriate β, γ, it is ensured that the vehicle reaches the same position at the end of the operating condition. This is important for both approaches (using fixed torque demand and flexible torque demand, respectively) to maintain traffic efficiency.
(2) Better fuel economy is achieved.
Generally the appropriate β, γ should be determined by balancing traffic efficiency and fuel economy. The optimal constant values of beta and gamma are changed along with different driving conditions. And gamma requires a priori knowledge of the overall driving conditions in order to determine the optimum beta. In order to ensure the constraint conditions (equation (34)) and good working condition adaptability, an adaptive law based on the torque demand and the driving distance change is designed, as shown in equations (37) and (38), the concept of Proportional Integral (PI) controller is utilized to adjust beta and gamma, and then:
Figure BDA0002958199600000171
Figure BDA0002958199600000172
where β (k) is a torque coefficient at time k, γ (k) is a speed coefficient at time k (k is 1,2,3 …), and β (k) is a torque coefficient at time k0，γ0Is the initial value of the time-domain clock,
Figure BDA0002958199600000173
is the optimum compliant torque requirement, TdemIs the fixed torque demand generated from the upper speed controller,
Figure BDA0002958199600000174
and x is the distance traveled for the soft and fixed torque requirements, Kp，KiIs a coefficient;
and (3) substituting the expressions (37) and (38) into the expression (28) to obtain the corresponding engine output torque and the corresponding motor torque, namely realizing the self-adaptive energy management of the intelligent hybrid electric vehicle.
The EMS of a conventional HEV is input by the driver's torque demand or wheel drive torque, regardless of speed prediction. And a-HEVs plan the speed of the vehicle by taking into account traffic conditions. Its energy management has more control freedom in improving fuel efficiency. The speed of A-HEVs in the intelligent hybrid electric vehicle adaptive energy management method is designed with the aim of safety. The fixed torque requirement means that the EMS accurately tracks the torque requirement from an upper controller, and the flexible torque requirement is to introduce a certain variable quantity on the basis of the fixed torque requirement and aim at seeking the instantaneous optimal torque requirement and then carry out torque distribution. The energy management of the parallel a-HEVs consists of two layers. An upper level controller (e.g., LQR, MPC) generates vehicle speed as an input to the powertrain energy management strategy of the lower level controller. A certain variable quantity is introduced on the basis of a fixed torque demand, the aim is to optimize and improve the torque distribution of the ECMS, and an adaptive law is designed to obtain better control performance. Hybrid vehicle dynamics are fed back to the upper level controller to regulate speed.
The invention discloses an adaptive energy management method for an intelligent hybrid electric vehicle, which mainly relates to modeling of parallel A-HEVs, cooperatively optimizes a gear shifting command and torque distribution based on an Equivalent Consumption Minimization Strategy (ECMS) to extract a gear shifting diagram, and considers the adaptive energy management strategy for the A-HEVs with flexible torque requirements and battery life. The main contents in the modeling of the parallel A-HEVs are as follows: modeling engine, electric machine, battery, transmission and vehicle dynamics. The main contents of cooperatively optimizing the gear shift command and the torque distribution to obtain the gear shift diagram based on the ECMS are as follows: the shift command and torque distribution are optimized in consideration of drivability and fuel economy of the vehicle, so that an optimal shift map is obtained. The main contents of the A-HEVs adaptive energy management method considering the flexible torque requirement and the battery life are as follows: based on the optimized gear shifting diagram, the flexible torque requirement and the battery life are considered, the improved ECMS is adopted, the torque is optimally distributed, and the self-adaptive law of main parameters is designed. The invention has the beneficial effects that: compared with the traditional A-HEVs energy management algorithm, the method has better fuel economy and optimizes the service life of the battery on the premise of ensuring good driving performance and traffic efficiency.

Claims (5)

1. An intelligent hybrid electric vehicle adaptive energy management method is characterized by comprising the following steps:
step 1, establishing a parallel A-HEVs model of a single-shaft parallel hybrid electric vehicle;
step 2, optimizing a gear shifting command and torque distribution by adopting an ECMS (electric control mechanical systems), and combining a parallel A-HEVs (automatic gearbox control systems) model to extract an optimized gear shifting diagram;
step 3, developing an improved ECMS with flexible torque requirements and considering battery life by optimizing a gear shifting diagram;
and 4, designing the self-adaptive law of the main parameters in the objective function of the improved ECMS to realize the self-adaptive energy management of the intelligent hybrid electric vehicle.
2. The adaptive energy management method for the intelligent hybrid electric vehicle according to claim 1, wherein the specific process of the step 1 is as follows: respectively establishing mathematical models for an engine, a motor, a battery, a transmission and vehicle dynamics of the single-shaft parallel hybrid electric vehicle;
(1) establishing a mathematical model of engine efficiency and engine speed and engine output torque for the engine:
the engine efficiency is determined by the engine speed and the engine output torque, as shown in equation (1);
ηe＝f(ne,Te) (1)
the engine output torque is calculated by equation (2);
Te＝αTemax(ne) (2)
fitting a curve of the maximum torque of the engine by the formula (3), and obtaining a fitting coefficient by an MATLAB curve fitting tool;
Temax＝k0+k1cos(ne·c)+p1sin(ne·c)+k2cos(2ne·c)+p2sin(2ne·c) (3)
wherein n iseIs the engine speed, TeIs the engine output torque, ηeIs the engine efficiency, alpha is the engine throttle opening, Temax(ne) Is the maximum torque of the engine at the current speed, c, k0，k1，k2，p1，p2Is a fitting coefficient;
(2) establishing a relation model of battery power and motor rotating speed:
according to the functional relation among the motor efficiency, the motor rotating speed and the motor torque, the method comprises the following steps:
ηm＝ψ(nm,Tm) (4)
the relation model of the battery power and the motor rotating speed is expressed as;
Figure FDA0002958199590000021
wherein n ismIndicating motor speed, TmRepresenting motor torque, ηmIndicating motor efficiency, PbRepresents the power required by the battery;
(3) establishing a state of charge dynamic equation for the battery:
Figure FDA0002958199590000022
wherein R isinIs the internal resistance of the battery, VocIs an open circuit voltage, QmaxIs the maximum capacity;
(4) modeling torque and speed for the transmission:
the rotating parts are assumed to be rigid and expressed in concentrated mass, and the torque expression of the transmission is as follows:
Figure FDA0002958199590000023
the speed expression of the transmission is:
win＝woutig(Gear)i0 (8)
wherein, ToutRepresenting the output torque, T, of the transmissioninRepresenting transmission input shaft torque, ηGRRepresenting transmission efficiency, ig(Gear) denotes each transmission Gear ratio, i0Representing the main reducer transmission ratio, Gear representing the number of gears, winRepresenting angular speed, w, of the input shaft of the transmissionoutRepresenting transmission output shaft angular speed;
(5) under the requirements of the fixed torque of the transmission and the flexible torque of the transmission, a vehicle dynamic model is established:
Figure FDA0002958199590000031
Figure FDA0002958199590000032
nin(t)＝30igi0va(t)/πr (11)
wherein,
Figure FDA0002958199590000033
indicating a compliant torque request, T, on the transmission input shaftdem(t) represents a fixed torque demand on the transmission input shaft, CDRepresenting the coefficient of air resistance, A representing the frontal area, va(t) represents the longitudinal vehicle speed generated by the upper level controller,
Figure FDA0002958199590000034
representing variable longitudinal vehicle speed, m representing vehicle trim mass, f representing rolling resistance coefficient, delta representing rotating mass coefficient, r representing wheel radius, ninRepresenting the speed of the transmission input shaft, FbrakeIndicating the braking force.
3. The intelligent hybrid electric vehicle adaptive energy management method according to claim 2, characterized in that the step 2 comprises the following specific processes:
step 2.1, establishing an objective function of the gear shifting diagram:
Figure FDA0002958199590000035
wherein s (t) is an equivalent factor, Pb(ug(t)) represents the battery power, QLHVRepresents a lower heating value of the fuel;
step 2.2, solving the optimized torque distribution and the optimized gear shifting command;
(1) optimizing the distribution process of the output torque of the engine and the torque of the motor, wherein the torque of the input shaft of the transmission meets the constraint conditions as follows:
Figure FDA0002958199590000036
Tdem＝Tv/ig(Gear)i0. (14)
in the formulae (14) and (15), TdemRepresenting transmission input shaft torque, TvRepresenting torque at the wheels;
(2) the optimized gear shifting command sh (t) comprises the following processes:
the optimal gear is obtained by equation (16), and is constrained by equation (17):
g(t)＝g(t-1)+sh(t),sh(t)∈{-1,0,1} (15)
1≤g(t)≤5. (16)
wherein, the numerical value { -1,0,1} respectively represents a downshift, a constant gear and an upshift;
step 2.3, obtaining the optimal control input u according to the optimized gear shifting command and the output torque of the engine under the constraint condition of meeting the torque of the input shaft of the transmissiong(t)，
ug(t)＝[Te(t)，sh(t)] (18)；
Step 2.4, inputting u according to the optimal controlg(t) and equation (12), obtaining an objective function of the optimized shift diagram and a constraint condition satisfied by the objective function:
the objective function is:
uopt(t)＝min{L[ug(t),s(t)]} (17)
the constraint is expressed as:
Figure FDA0002958199590000041
wherein, Tm(T) represents motor torque, Te(t) represents engine output torque; t ism_min(nm(T)) represents the minimum torque of the motor at the current speed, Tm_max(nm(t)) represents the motor torque capacity at the current speed; t ise_max(ne(t)) represents the engine torque capacity at the current speed; n ism_maxRepresenting the maximum speed of the motor, ne_minRepresenting minimum engine speed, ne_maxRepresenting a maximum engine speed; SOCminIndicating minimum state of charge, SOCmaxIndicating the maximum state of charge.
4. The adaptive energy management method for the intelligent hybrid electric vehicle according to claim 2, wherein the specific process of the step 3 is as follows: the battery capacity loss Q during battery aging is calculated by adopting a cyclic semi-empirical battery aging modelloss：
Figure FDA0002958199590000051
Wherein QlossIs the battery capacity loss, α, β and η are fitting coefficients related to SOC, EaIs an activation energy, RgIs the gas constant, T is the cell temperature, Ah is the cumulative charge throughput, IrateIs the current velocity, z is the power law factor;
wherein,
Figure FDA0002958199590000052
Figure FDA0002958199590000053
wherein, Ib(t) represents current, Q represents maximum charge capacity;
under the nominal condition of Irate,norm＝2.5[1/h]，SOCnorm0.35 and TnormThe nominal battery life is calculated (273.15+25) K:
Figure FDA0002958199590000054
the actual battery life is defined in terms of Ah throughput associated with a particular operating condition, and is expressed as:
Figure FDA0002958199590000055
and calculating the aging intensity coefficient of the battery according to the nominal service life and the actual service life of the battery:
Figure FDA0002958199590000056
the objective function of the improved ECMS is designed based on the above parameters as:
Figure FDA0002958199590000057
wherein β, γ are coefficients, and u (T) ═ Te_opt,Tm_optK is a weighting factor for the aging cost of the battery, CaIs the ratio of the replacement cost of the battery to the price of 1 kilogram of gasoline;
minimizing the objective function of the instantaneous modified ECMS yields:
[Te_opt,Tm_opt]＝min{L[u(t)]} (26)
by obtaining the amount of change in the vehicle speed by equations (9) and (10), the vehicle dynamics model is represented by equation (29):
Figure FDA0002958199590000061
the amounts of change in the vehicle speed, the travel distance, and the transmission torque request are obtained by equations (30) to (32):
Figure FDA0002958199590000062
Figure FDA0002958199590000063
Figure FDA0002958199590000064
further obtaining:
Figure FDA0002958199590000065
assuming that the vehicle with the compliant torque request and the fixed torque request should reach the same position at the end of the operating condition, the relationship between Δ x and Δ v is expressed as:
Figure FDA0002958199590000066
and determining the constraint conditions of the flexible torque requirement, namely the constraint conditions of the improved ECMS are as follows:
Figure FDA0002958199590000067
the amount of change in compliance torque is expressed as:
Figure FDA0002958199590000068
limited by the maximum torque of the engine and the electric machine at the current speed;
where b, b are the lower and upper limits, T, respectively, of the flexible transmission torque requeste_opt，Tm_optEngine output torque and motor torque, respectively, for the flexible torque demand strategy.
5. The intelligent hybrid electric vehicle adaptive energy management method according to claim 4, characterized in that the specific process of step 4 is as follows:
using the concept of Proportional Integral (PI) controller to adjust the coefficients β, γ, there are:
Figure FDA0002958199590000071
Figure FDA0002958199590000072
where β (k) is a torque coefficient at time k, γ (k) is a speed coefficient at time k (k is 1,2,3 …), and β (k) is a torque coefficient at time k0，γ0Is the initial value of the time-domain clock,
Figure FDA0002958199590000073
is the optimum compliant torque requirement, TdemIs the fixed torque demand generated from the upper speed controller,
Figure FDA0002958199590000074
and x is the distance traveled for the soft and fixed torque requirements, Kp，KiIs a coefficient;
and (3) substituting the expressions (37) and (38) into the expression (28) to obtain the corresponding engine output torque and the corresponding motor torque, namely realizing the self-adaptive energy management of the intelligent hybrid electric vehicle.
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