CN104627167B - 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 PDFInfo
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- CN104627167B CN104627167B CN201510043860.8A CN201510043860A CN104627167B CN 104627167 B CN104627167 B CN 104627167B CN 201510043860 A CN201510043860 A CN 201510043860A CN 104627167 B CN104627167 B CN 104627167B
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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
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
Technical field
The present invention relates to hybrid electric vehicle energy management technical field, especially relate to a kind of mixing considering battery life
Power car energy management method and system.
Background technology
The development of the novel green automobile of prominent promotion environmental protection and energy saving of the rise of fossil fuel price and environmental problem.Pure
Electric motor car, hybrid electric vehicle and fuel-cell vehicle all can as novel green automobile, compared with orthodox car, their efficiency highs,
Discharge is few, it has also become the new trend of development of automobile industry.Pure electric vehicle is ripe not enough due to current driving force battery technology, such as
Endurance is not enough, battery security is poor, these problems of battery life, still can not push in large area apply；Fuel-cell vehicle
It is even more due to the technology barrier such as fuel storage and energy conversion, and the reason such as the supporting imperfection of related infrastructure, serious hinder
Hinder its development.Hybrid electric vehicle is the new forms of energy vehicle of current main flow.
The energy management strategies of one side hybrid electric vehicle are the brains of full-vehicle control, the work of each power source of its Collaborative Control
Make state, on the premise of ensureing dynamic property, safety and the comfortableness of car load, seek efficiency highest, minimum emissions.Domestic mixed
The research closing power car energy management strategies aspect is primarily directed to the energy management of series and parallel hybrid electric vehicle, right
Energy management research in mixed connection formula hybrid electric vehicle still belongs to blank.And the control algolithm of studies in China mainly uses and is based on
Rule or the control strategy of intelligent algorithm.Achievement in research for the control strategy based on optimization method is still immature,
With abroad there is larger gap.
Due to long-term in the state of discharge and recharge, the length in its life-span also becomes the electrokinetic cell of another aspect hybrid electric vehicle
For the problem needing emphasis to consider in hybrid electric vehicle energy management.And life-span and the combustion of electrokinetic cell is thought in common research
It is conflicting between the consumption of oil, how to weigh the relation between both, be also a new problem being worth research.
Traditional energy management strategies are only concerned the criterion of the fuel consumption, and the operation conditions for battery but considers seldom.And study and show oil consumption
The relation of the life-span of an economy and battery really contradiction.If being only concerned oil consumption problem in energy management, then can make
Battery is in less healthy running status, affects its normal use time.
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 to consider the hybrid electric vehicle energy management plan of cell health state
Slightly, but because it does not account for the impact of battery life cost, therefore control, control effect cannot be coordinated with oil consumption cost
Unsatisfactory.
Content of the invention
The purpose of the present invention is exactly to provide to overcome the defect that above-mentioned prior art exists that a kind of control effect is good, have
Effect improves battery life, reduces vehicle and using the consideration hybrid electric vehicle energy management method of battery life of totle drilling cost and be
System.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of hybrid electric vehicle energy management method considering battery life, comprises the following steps：
1) collection current vehicle operating status data and battery operation status data；
2) set up auto model, and according to travel condition of vehicle and battery in described auto model prediction following a period of time
Running status；
3) calculate battery capacity decay cost summation and oil consumption cost summation in following a period of time；
4) set up multi objective control model, obtain the optimum control meeting optimization aim using multi-objective coordinated control algolithm
Amount, described multi objective control model includes object function J*With constraint condition C,
Described object function J*For：J*=min (WfJf+WbJb)；
Described constraints C includes：xmin≤xk≤xmax, ymin≤yk≤ymaxAnd umin≤uk≤umax；
Wherein, JfFor oil consumption cost summation, JbFor cell decay cost summation, WfFor the weights of oil consumption cost, WbFor battery
The weights of Life Cost, xkFor the quantity of state of k moment auto model, xmin、xmaxIt is respectively minima and the maximum of quantity of state,
ykFor the output of k moment auto model, ymin、ymaxIt is respectively minima and the maximum of output, ukFor k moment vehicle mould
The controlled quentity controlled variable of type, umin、umaxIt is respectively minima and the maximum of controlled quentity controlled variable；
5) control signal is formed according to optimum control amount, control the running status of vehicle.
Described travel condition of vehicle data includes speed, engine speed and motor speed；
Described battery operation status data includes battery dump energy, battery capacity attenuation, battery current and battery electricity
Pressure.
Described auto model is specially
Wherein, x is the quantity of state of auto model, and u is the controlled quentity controlled variable of auto model, and v is the known quantity of auto model, y table
Show the output of auto model, f () represents the state transition equation of auto model, represent that current state transfers to NextState
Procedure function, g () represents the output equation of auto model, represents between output and controlled quentity controlled variable, quantity of state and known quantity
Functional relationship.
Described quantity of state includes engine speed, motor speed and battery dump energy；
Described output includes vehicle present speed, current oil consumption and present battery capacity attenuation value；
Described controlled quentity controlled variable includes engine throttle opening, braking torque and motor torque；
Described known quantity includes vehicle current goal speed and current demand power.
Described battery capacity decay cost is obtained by below equation：
Qloss=b (x, u)
Wherein, QlossRepresent battery capacity pad value, b () represent capacity attenuation value and auto model quantity of state x and
Functional relationship between controlled quentity controlled variable u；
Described oil consumption cost is obtained by below equation：
Wherein,Represent fuel consumption values, between the quantity of state x of m () expression fuel consumption values and auto model and controlled quentity controlled variable u
Functional relationship.
A kind of hybrid electric vehicle EMS considering battery life, including：
Data acquisition module, for gathering current vehicle operating status data and battery operation status data；
Top level control device, for the data of receiving data acquisition module collection, when predicting following one section according to auto model
Between quantity of state and the cell decay cost of following a period of time and oil consumption cost, then by multi-objective coordinated control algolithm meter
Calculate Optimal Control amount；
Lower floor's controller group, for receiving the Optimal Control amount of top level control device calculating, controls the running status of vehicle.
Described lower floor controller group includes throttle control, brake controller and electric machine controller.
Compared with prior art, the present invention has advantages below：
(1) present invention considers battery life, directly adds battery life cost in object function, using model prediction
Control algolithm obtains the controlled quentity controlled variable of optimum, can optimized distribution energy, effectively improve battery life, reduce the assembly that vehicle uses
This.
(2) control effect good Controlling model is more accurate it is considered to vehicle dynamics characteristics, and adopt real-time optimization control
Algorithm (Model Predictive Control Algorithm) processed, it is considered to the constraints of vehicle traveling on the premise of ensureing real-time, improves and controls
Performance.
(3) the vehicle oil consumption cost that the hybrid electric vehicle energy control method of the present invention considers simultaneously becomes with battery life
This, and two kinds of costs are carried out with optimization coordination control.In the case of ensureing that oil consumption cost is more or less the same, reduce battery life
Cost.
(4) there is optimization and practicality simultaneously, in the premise ensureing speed tracking, improve the fuel-economy of vehicle
Property and battery life.
(5) inventive algorithm calculates time real-time height, may apply in actual vehicle.
Brief description
Fig. 1 is the schematic flow sheet of the present invention；
Fig. 2 is the system principle diagram of the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, give detailed embodiment and specific operating process, but protection scope of the present invention be not limited to
Following embodiments.
As shown in figure 1, the present embodiment provide a kind of consider battery life hybrid electric vehicle energy management method, according to work as
Vehicle in front driving information and battery status information, performance model forecast Control Algorithm coordinates to control oil consumption and the battery capacity of vehicle
Decay cost function, obtains optimal controlled quentity controlled variable, comprises the following steps that：
In step S1, gather current vehicle operating status data and battery operation status data.Described travel condition of vehicle
Data includes speed, engine speed and motor speed etc.；Described battery operation status data includes battery dump energy, battery
Capacity attenuation amount, battery current and cell voltage etc..
In step S2, set up auto model, and shape is run according to vehicle in described auto model prediction following a period of time
State and battery operation state.
Auto model is specially：
Wherein, x is the quantity of state of auto model, including but not limited to engine speed, motor speed and remaining battery electricity
Amount etc., u is the controlled quentity controlled variable of auto model, including but not limited to engine throttle opening, braking torque and motor torque etc., and v is
The known quantity of auto model, including but not limited to vehicle current goal speed and current demand power etc., y represents auto model
Output, including but not limited to vehicle present speed, current oil consumption and present battery capacity attenuation value etc., f () represents vehicle
The state transition equation of model, represents that current state transfers to the procedure function of NextState, g () represents the defeated of auto model
Go out equation, represent the functional relationship between output and controlled quentity controlled variable, quantity of state and known quantity.
Hypothesis current state amount is x0And one section of N of futurecThe controlled quentity controlled variable of timeUsing above-mentioned vehicle
Model can obtain following one section of NpThe quantity of state of timeAnd output
In step S3, calculate battery capacity decay cost summation and oil consumption cost summation in following a period of time.
Described battery capacity decay cost is obtained by below equation：
Qloss=b (x, u)
Wherein, QlossRepresent battery capacity pad value, b () represent capacity attenuation value and auto model quantity of state x and
Functional relationship between controlled quentity controlled variable u, its dump energy with battery, electric current, voltage and battery cell temperature etc. have direct relation；
Wherein,Represent fuel consumption values, between the quantity of state x of m () expression fuel consumption values and auto model and controlled quentity controlled variable u
Functional relationship, its torque with electromotor and rotating speed etc. have direct relation.
So following one section of NpIn time, oil consumption cost summation JfWith cell decay cost JbCan be carried out by equation below
Calculate：
In step S4, set up multi objective control model, obtained using multi-objective coordinated control algolithm and meet optimization aim
Optimum control amount, described multi objective control model includes object function J*With constraint condition C,
Described object function J*For：J*=min (WfJf+WbJb)；
Described constraints C includes：xmin≤xk≤xmax, ymin≤yk≤ymaxAnd umin≤uk≤umax；
Wherein, JfFor oil consumption cost summation, JbFor cell decay cost summation, WfFor the weights of oil consumption cost, WbFor battery
The weights of Life Cost, xkFor the quantity of state of k moment auto model, xmin、xmaxIt is respectively minima and the maximum of quantity of state,
ykFor the output of k moment auto model, ymin、ymaxIt is respectively minima and the maximum of output, ukFor k moment vehicle mould
The controlled quentity controlled variable of type, umin、umaxIt is respectively minima and the maximum of controlled quentity controlled variable.
After setting up multi objective control model, by optimization problem be transformed into object function be J, constraints be C two
Secondary planning problem, is solved using active-set method and obtains optimal solution, be i.e. optimum control increment Delta u, then needed for current time k
Optimum control amount u (k)=u (k-1)+Δ u.
In step S5, form the control signal of each controller according to optimum control amount, control the running status of vehicle.
As shown in Fig. 2 the present embodiment also provides a kind of hybrid electric vehicle EMS considering battery life, including
Data acquisition module 1, top level control device 2 and lower floor's controller group 3, data acquisition module 1 is used for gathering Current vehicle operation shape
State data and battery operation status data；Top level control device 2 is used for the data of receiving data acquisition module 1 collection, according to vehicle
The cell decay cost of the quantity of state of model prediction a period of time in future and following a period of time and oil consumption cost, then by many
Goal coordination control algolithm calculates Optimal Control amount；Lower floor's controller group 3 is used for receiving the Optimal Control of top level control device 2 calculating
Amount, controls the running status of vehicle.Lower floor's controller group includes throttle control, brake controller and electric machine controller etc..
Claims (7)
1. a kind of hybrid electric vehicle energy management method considering battery life is it is characterised in that comprise the following steps：
1) collection current vehicle operating status data and battery operation status data；
2) set up auto model, and according to travel condition of vehicle and battery operation in described auto model prediction following a period of time
State；
3) calculate battery capacity decay cost summation and oil consumption cost summation in following a period of time；
4) set up multi objective control model, the optimum control amount meeting optimization aim obtained using multi-objective coordinated control algolithm,
Described multi objective control model includes object function J*With constraint condition C,
Described object function J*For：J*=min (WfJf+WbJb)；
Described constraints C includes：xmin≤xk≤xmax, ymin≤yk≤ymaxAnd umin≤uk≤umax；
Wherein, JfFor oil consumption cost summation, JbFor battery capacity decay cost summation, WfFor the weights of oil consumption cost, WbFor battery
The weights of Life Cost, xkFor the quantity of state of k moment auto model, xmin、xmaxIt is respectively minima and the maximum of quantity of state,
ykFor the output of k moment auto model, ymin、ymaxIt is respectively minima and the maximum of output, ukFor k moment vehicle mould
The controlled quentity controlled variable of type, umin、umaxIt is respectively minima and the maximum of controlled quentity controlled variable；
5) control signal is formed according to optimum control amount, control the running status of vehicle.
2. the hybrid electric vehicle energy management method considering battery life according to claim 1 is it is characterised in that described
Travel condition of vehicle data includes speed, engine speed and motor speed；
Described battery operation status data includes battery dump energy, battery capacity attenuation, battery current and cell voltage.
3. the hybrid electric vehicle energy management method considering battery life according to claim 1 is it is characterised in that described
Auto model is specially：
Wherein, x is the quantity of state of auto model, and u is the controlled quentity controlled variable of auto model, and v is the known quantity of auto model, and y represents car
The output of model, f () represents the state transition equation of auto model, represents that current state transfers to the mistake of NextState
Eikonal number, g () represents the output equation of auto model, represents output and controlled quentity controlled variable, quantity of state, the known quantity of auto model
Between functional relationship.
4. the hybrid electric vehicle energy management method considering battery life according to claim 3 is it is characterised in that described
Quantity of state includes engine speed, motor speed and battery dump energy；
Described output includes vehicle present speed, current oil consumption and present battery capacity attenuation value；
Described controlled quentity controlled variable includes engine throttle opening, braking torque and motor torque；
Described known quantity includes vehicle current goal speed and current demand power.
5. the hybrid electric vehicle energy management method considering battery life according to claim 1 is it is characterised in that described
Battery capacity decay cost is obtained by below equation：
Qloss=b (x, u)
Wherein, QlossRepresent battery capacity pad value, the quantity of state x of b () expression capacity attenuation value and auto model and controlled quentity controlled variable
Functional relationship between u；
Described oil consumption cost is obtained by below equation：
Wherein,Represent fuel consumption values, the function between the quantity of state x of m () expression fuel consumption values and auto model and controlled quentity controlled variable u closes
System.
6. a kind of realize considering the system of the hybrid electric vehicle energy management method of battery life as claimed in claim 1, its
It is characterised by, including：
Data acquisition module, for gathering current vehicle operating status data and battery operation status data；
Top level control device, for the data of receiving data acquisition module collection, predicts following a period of time according to auto model
The battery capacity decay cost summation of quantity of state and following a period of time and oil consumption cost summation, then by multi-objective coordinated control
Algorithm processed calculates Optimal Control amount；
Lower floor's controller group, for receiving the Optimal Control amount of top level control device calculating, controls the running status of vehicle.
7. system according to claim 6 is it is characterised in that described lower floor controller group includes throttle control, brake
Controller and electric machine controller.
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| CN108091951B (en) * | 2017-12-29 | 2020-04-03 | 潍柴动力股份有限公司 | Battery management system and control method thereof |
| CN108674411A (en) * | 2018-07-03 | 2018-10-19 | 肖金保 | A kind of Energy Management System for Hybrid Electric Vehicle |
| CN110254418B (en) * | 2019-06-28 | 2020-10-09 | 福州大学 | Hybrid electric vehicle reinforcement learning energy management control method |
| CN110509914B (en) * | 2019-09-16 | 2020-08-04 | 重庆邮电大学 | Energy consumption optimization method for parallel hybrid electric vehicle |
| CN112757964A (en) * | 2019-10-17 | 2021-05-07 | 郑州宇通客车股份有限公司 | Hybrid vehicle parameter configuration method and computer readable medium |
| CN110775065B (en) * | 2019-11-11 | 2020-09-29 | 吉林大学 | Hybrid electric vehicle battery life prediction method based on working condition recognition |
| CN110775043B (en) * | 2019-11-11 | 2020-09-29 | 吉林大学 | Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition |
| CN111007402B (en) * | 2019-12-03 | 2020-10-30 | 清华大学 | Durability test method for fuel cell stack |
| CN112991574B (en) * | 2019-12-13 | 2022-08-23 | 北京亿华通科技股份有限公司 | Method for analyzing attenuation of electric pile |
| CN110920601B (en) * | 2019-12-17 | 2021-03-30 | 北京交通大学 | Method for optimizing and controlling energy allocation of multi-anisotropy power source system |
| CN111619545B (en) * | 2020-05-08 | 2021-10-01 | 北京航空航天大学 | Hybrid electric vehicle energy management method based on traffic information |
| CN112319462B (en) * | 2020-11-17 | 2022-05-13 | 河南科技大学 | Energy management method for plug-in hybrid electric vehicle |
| CN112677956B (en) * | 2020-12-31 | 2022-03-25 | 吉林大学 | Real-time optimization control method of planet series-parallel hybrid vehicle considering battery life |
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