CN104627167A - Hybrid vehicle energy managing method and system considering service life of battery - Google Patents

Hybrid vehicle energy managing method and system considering service life of battery Download PDF

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CN104627167A
CN104627167A CN201510043860.8A CN201510043860A CN104627167A CN 104627167 A CN104627167 A CN 104627167A CN 201510043860 A CN201510043860 A CN 201510043860A CN 104627167 A CN104627167 A CN 104627167A
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battery
vehicle
auto model
state
cost
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CN104627167B (en
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王峻
麻斯韦
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Tongji University
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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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
    • B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • 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
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B60W2510/244—Charge state
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/06—Combustion engines, Gas turbines
    • B60W2710/0605—Throttle position
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/08—Electric propulsion units
    • B60W2710/083—Torque
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/18—Braking system
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/62—Hybrid vehicles

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention relates to a hybrid vehicle energy managing method considering the service life of a battery. The method includes the following steps of firstly, collecting current vehicle running state data and battery running state data; secondly, establishing a vehicle model, and predicting the vehicle running state and the battery running state within a period of time in the future according to the vehicle model; thirdly, calculating the sum of battery capacity attenuation cost and the sum of oil consumption cost within a period of time in the future; fourthly, establishing a multi-target control model, and obtaining the optimal control amount meeting the optimization target through a multi-target coordination control algorithm, wherein the multi-target control model comprises a target function J* and a constraint condition C; fifthly, forming a control signal according to the optimal control amount, and controlling the running state of a vehicle. Compared with the prior art, the method has the advantages of being good in control effect, capable of effectively prolonging the service life of the battery, reducing the total use cost of the vehicle, and the like.

Description

A kind of hybrid electric vehicle energy management method and system considering battery life
Technical field
The present invention relates to hybrid electric vehicle energy management technical field, especially relate to a kind of hybrid electric vehicle energy management method and the system of considering battery life.
Background technology
The development of the novel green automobile of the rise of fossil fuel price and the outstanding promotion environmental protection and energy saving of environmental problem.Pure electric vehicle, hybrid electric vehicle and fuel-cell vehicle all can be used as novel green automobile, and compared with orthodox car, their efficiency is high, discharge is few, has become the new trend of development of automobile industry.Pure electric vehicle due to current driving force battery technology ripe not enough, such as flying power is not enough, battery security is poor, these problems of battery life, still can not push application in large area to; Fuel-cell vehicle is especially due to the technology barrier such as fuel conservation and transformation of energy, and the reason such as the supporting imperfection of related infrastructure, seriously hinders its development.Hybrid electric vehicle is the new forms of energy vehicle of current main flow.
On the one hand the energy management strategies of hybrid electric vehicle is the brain of full-vehicle control, the mode of operation of each propulsion source of its Collaborative Control, under the prerequisite ensureing the dynamic property of car load, safety and traveling comfort, seeks most effective, minimum emissions.The research of domestic hybrid electric vehicle energy management strategies aspect is mainly for the energy management of tandem and parallel type hybrid vehicle, and the energy management research for mixed connection formula hybrid electric vehicle still belongs to blank.And the control algorithm of studies in China mainly uses control policy that is rule-based or intelligent algorithm.Achievement in research for the control policy based on optimization method is still immature, and there is larger gap abroad.
The electrokinetic cell of hybrid electric vehicle is due to long-term in the state of discharge and recharge on the other hand, and the length in its life-span also becomes the problem needing emphasis to consider in hybrid electric vehicle energy management.And common research thinks that between life-span of electrokinetic cell and the consumption of fuel oil be conflicting, how to weigh the relation between both, it is also a new problem being worth research.Traditional energy management strategies is only concerned about the criterion of the fuel consumption, and the operation conditions for battery is but considered seldom.And the relation of the life-span that research shows an oil consumption economy and battery contradiction really.If be only concerned about oil consumption problem in energy management, battery so can be made to be in not too healthy running state, to affect its normal period of service.
Document Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles (S.Ebbesen, P.Elbert and L.Guzzella, .IEEE Trans.on Vehicular Technology, 2012.61 (7): p.2893-2900) propose the hybrid electric vehicle energy management strategies considering cell health state, but because it does not consider the impact of battery life cost, therefore cannot carry out cooperation control with oil consumption cost, control effects is unsatisfactory.
Summary of the invention
Object of the present invention be exactly provide to overcome defect that above-mentioned prior art exists a kind of control effects good, effectively improve battery life, reduce the hybrid electric vehicle energy management method of consideration battery life and system that vehicle uses total cost.
Object of the present invention can be achieved through the following technical solutions:
Consider a hybrid electric vehicle energy management method for battery life, comprise the following steps:
1) current vehicle operating status data and battery operation status data is gathered;
2) auto model is set up, and according to travel condition of vehicle and battery operation state in described auto model prediction following a period of time;
3) capacity of cell decay cost summation and oil consumption cost summation in following a period of time is calculated;
4) set up multi objective control model, adopt multi-objective coordinated control algorithm to obtain the optimal control amount meeting optimization aim, described multi objective control model comprises objective function J *with constraint condition C,
Described objective function J *for: J *=min (W fj f+ W bj b);
Described constraint condition C comprises: x min≤ x k≤ x max, y min≤ y k≤ y maxand u min≤ u k≤ u max;
Wherein, J ffor oil consumption cost summation, J bfor cell decay cost summation, W ffor the weights of oil consumption cost, W bfor the weights of battery life cost, x kfor the quantity of state of k moment auto model, x min, x maxbe respectively minimum value and the maxim of quantity of state, y kfor the output of k moment auto model, y min, y maxbe respectively minimum value and the maxim of output, u kfor the controlling quantity of k moment auto model, u min, u maxbe respectively minimum value and the maxim of controlling quantity;
5) according to optimal control amount formation control signal, the running state of vehicle is controlled.
Described travel condition of vehicle data comprise the speed of a motor vehicle, engine speed and motor speed;
Described battery operation status data comprises battery dump energy, capacity of cell decrement, battery current and cell pressure.
Described auto model is specially
x · = f ( x , u , v ) y = g ( x , u , v )
Wherein, x is the quantity of state of auto model, u is the controlling quantity of auto model, v is the known quantity of auto model, y represents the output of auto model, and f () represents the state transition equation of auto model, represents that current state transfers to the procedure function of NextState, g () represents the output equation of auto model, represents output and controlling quantity, functional relation between quantity of state and known quantity.
Described quantity of state comprises engine speed, motor speed and battery dump energy;
Described output comprises vehicle present speed, current oil consumption and present battery capacity attenuation value;
Described controlling quantity comprises engine throttle opening, braking torque and motor torque;
Described known quantity comprises vehicle current goal speed and current demand power.
Described capacity of cell decay cost is obtained by following formula:
Q loss＝b(x,u)
Wherein, Q lossrepresent capacity of cell pad value, b () represents the functional relation between the quantity of state x of capacity attenuation value and auto model and controlling quantity u;
Described oil consumption cost is obtained by following formula:
m · f = m ( x , u )
Wherein, represent fuel consumption values, m () represents the functional relation between the quantity of state x of fuel consumption values and auto model and controlling quantity u.
Consider a hybrid electric vehicle energy management system for battery life, comprising:
Data acquisition module, for gathering current vehicle operating status data and battery operation status data;
Top level control device, for receiving the data of data collecting module collected, according to the quantity of state of auto model prediction following a period of time and the cell decay cost of following a period of time and oil consumption cost, then by multi-objective coordinated control algorithm calculating optimal control amount;
Lower floor's controller group, for receiving the optimal control amount that top level control device calculates, controls the running state of vehicle.
Described lower floor controller group comprises accel control, brake controller and electric machine controller.
Compared with prior art, the present invention has the following advantages:
(1) contemplated by the invention battery life, directly in objective function, add battery life cost, utilize Model Predictive Control Algorithm to obtain optimum controlling quantity, can distribute energy be optimized, effectively improve battery life, reduce the total cost that vehicle uses.
(2) control effects is good, and consider vehicle dynamics characteristics, Controlling model is more accurate, and adopt real-time optimal control algorithm (Model Predictive Control Algorithm), under the prerequisite ensureing real-time, consider the constraint condition that vehicle travels, improve controller performance.
(3) the vehicle oil consumption cost simultaneously considered of hybrid electric vehicle energy control method of the present invention and battery life cost, and optimization cooperation control is carried out to two kinds of costs.When ensureing that oil consumption cost is more or less the same, reduce battery life cost.
(4) there is optimum voltinism and practicality simultaneously, in the prerequisite ensureing speed tracking, improve fuel economy and the battery life of vehicle.
(5) algorithm real-time computing time of the present invention is high, can be applied in actual vehicle.
Accompanying drawing explanation
Fig. 1 is schematic flow sheet of the present invention;
Fig. 2 is system principle diagram of the present invention.
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.The present embodiment is implemented premised on technical solution of the present invention, give detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
As shown in Figure 1, the present embodiment provides a kind of hybrid electric vehicle energy management method considering battery life, according to Current vehicle driving information and battery status information, the oil consumption of performance model forecast Control Algorithm cooperation control vehicle and capacity of cell decay cost function, obtain best controlling quantity, concrete steps are as follows:
In step S1, gather current vehicle operating status data and battery operation status data.Described travel condition of vehicle data comprise the speed of a motor vehicle, engine speed and motor speed etc.; Described battery operation status data comprises battery dump energy, capacity of cell decrement, battery current and cell pressure etc.
In step S2, set up auto model, and according to travel condition of vehicle and battery operation state in described auto model prediction following a period of time.
Auto model is specially:
x · = f ( x , u , v ) y = g ( x , u , v )
Wherein, x is the quantity of state of auto model, include but not limited to engine speed, motor speed and battery dump energy etc., u is the controlling quantity of auto model, include but not limited to engine throttle opening, braking torque and motor torque etc., v is the known quantity of auto model, include but not limited to vehicle current goal speed and current demand power etc., y represents the output of auto model, include but not limited to vehicle present speed, current oil consumption and present battery capacity attenuation value etc., f () represents the state transition equation of auto model, represent that current state transfers to the procedure function of NextState, g () represents the output equation of auto model, represent output and controlling quantity, functional relation between quantity of state and known quantity.
Suppose that current state amount is x 0and following one section of N cthe controlling quantity of time utilize above-mentioned auto model can obtain following one section of N pthe quantity of state of time and output
In step S3, calculate capacity of cell decay cost summation and oil consumption cost summation in following a period of time.
Described capacity of cell decay cost is obtained by following formula:
Q loss＝b(x,u)
Wherein, Q lossrepresent capacity of cell pad value, b () represents the functional relation between the quantity of state x of capacity attenuation value and auto model and controlling quantity u, the dump energy of itself and battery, electric current, and voltage and battery cell temperature etc. have direct relation;
m · f = m ( x , u )
Wherein, represent fuel consumption values, m () represents the functional relation between the quantity of state x of fuel consumption values and auto model and controlling quantity u, and the torque and rotational speed etc. of itself and driving engine has direct relation.
So following one section of N pin time, oil consumption cost summation J fwith cell decay cost J bcan be calculated by formula below:
J f = Σ k = i N p m ( x k , u k ) J b = Σ k = i N p b ( x k , u k )
In step S4, set up multi objective control model, adopt multi-objective coordinated control algorithm to obtain the optimal control amount meeting optimization aim, described multi objective control model comprises objective function J *with constraint condition C,
Described objective function J *for: J *=min (W fj f+ W bj b);
Described constraint condition C comprises: x min≤ x k≤ x max, y min≤ y k≤ y maxand u min≤ u k≤ u max;
Wherein, J ffor oil consumption cost summation, J bfor cell decay cost summation, W ffor the weights of oil consumption cost, W bfor the weights of battery life cost, x kfor the quantity of state of k moment auto model, x min, x maxbe respectively minimum value and the maxim of quantity of state, y kfor the output of k moment auto model, y min, y maxbe respectively minimum value and the maxim of output, u kfor the controlling quantity of k moment auto model, u min, u maxbe respectively minimum value and the maxim of controlling quantity.
After setting up multi objective control model, optimization problem is just transformed to objective function is J, constraint condition is the quadratic programming problem of C, utilize active-set method to solve and obtain optimal solution, i.e. optimal control increment Delta u, then optimal control amount u (k)=u (k-1)+Δ u needed for current time k.
In step S5, form the control signal of each controller according to optimal control amount, control the running state of vehicle.
As shown in Figure 2, the present embodiment also provides a kind of hybrid electric vehicle energy management system considering battery life, comprise data acquisition module 1, top level control device 2 and lower floor's controller group 3, data acquisition module 1 is for gathering current vehicle operating status data and battery operation status data; The data that top level control device 2 gathers for receiving data acquisition module 1, according to the quantity of state of auto model prediction following a period of time and the cell decay cost of following a period of time and oil consumption cost, then by multi-objective coordinated control algorithm calculating optimal control amount; The optimal control amount that lower floor's controller group 3 calculates for receiving top level control device 2, controls the running state of vehicle.Lower floor's controller group comprises accel control, brake controller and electric machine controller etc.

Claims (7)

1. consider a hybrid electric vehicle energy management method for battery life, it is characterized in that, comprise the following steps:
1) current vehicle operating status data and battery operation status data is gathered;
2) auto model is set up, and according to travel condition of vehicle and battery operation state in described auto model prediction following a period of time;
3) capacity of cell decay cost summation and oil consumption cost summation in following a period of time is calculated;
4) set up multi objective control model, adopt multi-objective coordinated control algorithm to obtain the optimal control amount meeting optimization aim, described multi objective control model comprises objective function J *with constraint condition C,
Described objective function J *for: J *=min (W fj f+ W bj b);
Described constraint condition C comprises: x min≤ x k≤ x max, y min≤ y k≤ y maxand u min≤ u k≤ u max;
Wherein, J ffor oil consumption cost summation, J bfor cell decay cost summation, W ffor the weights of oil consumption cost, W bfor the weights of battery life cost, x kfor the quantity of state of k moment auto model, x min, x maxbe respectively minimum value and the maxim of quantity of state, y kfor the output of k moment auto model, y min, y maxbe respectively minimum value and the maxim of output, u kfor the controlling quantity of k moment auto model, u min, u maxbe respectively minimum value and the maxim of controlling quantity;
5) according to optimal control amount formation control signal, the running state of vehicle is controlled.
2. the hybrid electric vehicle energy management method of consideration battery life according to claim 1, is characterized in that, described travel condition of vehicle data comprise the speed of a motor vehicle, engine speed and motor speed;
Described battery operation status data comprises battery dump energy, capacity of cell decrement, battery current and cell pressure.
3. the hybrid electric vehicle energy management method of consideration battery life according to claim 1, is characterized in that, described auto model is specially:
x · = f ( x , u , v ) y = g ( x , u , v )
Wherein, x is the quantity of state of auto model, u is the controlling quantity of auto model, v is the known quantity of auto model, y represents the output of auto model, and f () represents the state transition equation of auto model, represents that current state transfers to the procedure function of NextState, g () represents the output equation of auto model, represents output and controlling quantity, functional relation between quantity of state and known quantity.
4. the hybrid electric vehicle energy management method of consideration battery life according to claim 3, is characterized in that, described quantity of state comprises engine speed, motor speed and battery dump energy;
Described output comprises vehicle present speed, current oil consumption and present battery capacity attenuation value;
Described controlling quantity comprises engine throttle opening, braking torque and motor torque;
Described known quantity comprises vehicle current goal speed and current demand power.
5. the hybrid electric vehicle energy management method of consideration battery life according to claim 1, is characterized in that, described capacity of cell decay cost is obtained by following formula:
Q loss＝b(x,u)
Wherein, Q lossrepresent capacity of cell pad value, b () represents the functional relation between the quantity of state x of capacity attenuation value and auto model and controlling quantity u;
Described oil consumption cost is obtained by following formula:
m · f = m ( x , u )
Wherein, represent fuel consumption values, m () represents the functional relation between the quantity of state x of fuel consumption values and auto model and controlling quantity u.
6. realize the system considering the hybrid electric vehicle energy management method of battery life as claimed in claim 1, it is characterized in that, comprising:
Data acquisition module, for gathering current vehicle operating status data and battery operation status data;
Top level control device, for receiving the data of data collecting module collected, according to the quantity of state of auto model prediction following a period of time and the cell decay cost of following a period of time and oil consumption cost, then by multi-objective coordinated control algorithm calculating optimal control amount;
Lower floor's controller group, for receiving the optimal control amount that top level control device calculates, controls the running state of vehicle.
7. the hybrid electric vehicle energy management system of consideration battery life according to claim 6, is characterized in that, described lower floor controller group comprises accel control, brake controller and electric machine controller.
CN201510043860.8A 2015-01-28 2015-01-28 Hybrid vehicle energy managing method and system considering service life of battery Expired - Fee Related CN104627167B (en)

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CN108674411A (en) * 2018-07-03 2018-10-19 肖金保 A kind of Energy Management System for Hybrid Electric Vehicle
CN110254418A (en) * 2019-06-28 2019-09-20 福州大学 A kind of hybrid vehicle enhancing study energy management control method
CN110509914A (en) * 2019-09-16 2019-11-29 重庆邮电大学 A kind of energy consumption optimization method of parallel hybrid electric vehicle
CN110775065A (en) * 2019-11-11 2020-02-11 吉林大学 Hybrid electric vehicle battery life prediction method based on working condition recognition
CN110775043A (en) * 2019-11-11 2020-02-11 吉林大学 Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition
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