CN110775043B - Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition - Google Patents

Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition Download PDF

Info

Publication number
CN110775043B
CN110775043B CN201911095653.1A CN201911095653A CN110775043B CN 110775043 B CN110775043 B CN 110775043B CN 201911095653 A CN201911095653 A CN 201911095653A CN 110775043 B CN110775043 B CN 110775043B
Authority
CN
China
Prior art keywords
working condition
battery
battery life
power
working
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911095653.1A
Other languages
Chinese (zh)
Other versions
CN110775043A (en
Inventor
宋大凤
杨丽丽
曾小华
王星琦
梁伟智
姜效望
宋美洁
陈虹旭
李量宇
吴梓乔
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jilin University
Original Assignee
Jilin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jilin University filed Critical Jilin University
Priority to CN201911095653.1A priority Critical patent/CN110775043B/en
Publication of CN110775043A publication Critical patent/CN110775043A/en
Application granted granted Critical
Publication of CN110775043B publication Critical patent/CN110775043B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0666—Engine power
    • 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
    • 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
    • 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
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • 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
    • 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
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • 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
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Hybrid Electric Vehicles (AREA)

Abstract

The invention discloses a hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition, which comprises the following steps: aiming at a hybrid automobile, energy management optimization control strategies under the working conditions of a Chinese passenger vehicle, a suburban (NEDC) working condition and a high-speed (HWFET) working condition are respectively formulated, the sum of the fuel consumption cost and the battery life attenuation cost of each stage is used as an optimization objective function, the battery life attenuation modes under all the working conditions are classified based on the result of the solution of a discrete dynamic programming algorithm, finally the battery life attenuation modes are identified based on a neural network, and the control strategy which can be applied to the real automobile on line in real time under the corresponding modes is established. The method provided by the invention extracts the battery life attenuation rule based on the working condition dimension, identifies the battery life attenuation mode, effectively slows down the battery life attenuation degree, ensures the fuel economy, and improves the accuracy of the system to the battery life prediction and the adaptability of the system to the actual driving working condition under the actual driving working condition.

Description

Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition
Technical Field
The invention belongs to the technical field of energy management of hybrid electric vehicles, and particularly relates to a hybrid electric vehicle energy optimization method based on battery life attenuation mode identification.
Background
The hybrid electric vehicle has a plurality of power sources, and the working states of the power sources need to be coordinated to meet the power requirement of the whole vehicle, so that the energy-saving advantage of the hybrid electric vehicle is fully exerted. The performance of the power battery directly influences the performance of the driving motor, so that the fuel economy and the emission performance of the whole vehicle are influenced, and the key for realizing the performance of the whole vehicle is realized. Research shows that fuel economy and battery life attenuation degree are mutually influenced, the current energy optimization management strategy mainly realizes the best fuel economy under specific working conditions by selecting proper performance indexes and optimization methods, and the influence of the power battery life attenuation on the fuel economy is rarely considered. In addition, the current research on the service life of the power battery mainly focuses on predicting the service life of the battery under a specific working condition so as to obtain the theoretical service life attenuation condition of the battery under the specific working condition, but in practice, due to the difference between the external environment, the driving habits and the driving modes, the actual driving working conditions of the hybrid electric vehicle are complex and changeable, so that the method for predicting the service life of the battery under a single working condition cannot be applied to a real vehicle on line in real time, and the accuracy and the real-time performance of the prediction of the service life of the battery cannot be considered at.
Currently, methods for predicting battery life are mainly classified into model-based estimation methods and data-based estimation methods. The model-based estimation method is characterized in that due to the fact that the actual driving working conditions of an automobile are complex and changeable, a model which is identical to the actual online working conditions of a battery is difficult to design to accurately predict the service life of the battery, for example, patent CN107878445A with publication date of 2018-4-6 discloses a hybrid electric automobile energy optimization management method considering battery performance attenuation. The data-based estimation method researches the battery life attenuation rule through a large amount of experimental data, has huge data processing amount and no working condition universality, can only describe the battery life attenuation rule in a single experiment or under a single working condition more accurately, and cannot represent the battery life attenuation rule under the actual driving working condition.
In addition, the energy management strategy of the hybrid electric vehicle is generally divided into a rule-based energy management strategy and an optimization-based energy management strategy, the rule-based energy management strategy is often applied to real vehicle control, but the rule determination has very large dependence on experience, and the dynamic programming global optimization management strategy with excellent domestic research effect is offline optimization and cannot be directly applied to real-time control.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a hybrid electric vehicle energy optimization method based on battery life attenuation mode identification, the relation between oil consumption and battery life attenuation is balanced and coordinated in an energy optimization management strategy, and the hybrid electric vehicle energy optimization method can be applied to a real vehicle on line in real time while the battery performance is optimized through identifying the battery life attenuation mode under the actual driving working condition, so that the fuel economy of the whole vehicle is improved while the dynamic property is ensured, the attenuation degree of the battery life is effectively reduced, and the accuracy of the battery life prediction is improved.
In order to solve the technical problems, the hybrid electric vehicle energy optimization method based on the battery life attenuation mode identification is realized by adopting the following technical scheme, and comprises the following steps:
(1) the method comprises the following steps of respectively carrying out global optimization control based on a dynamic programming algorithm aiming at the working condition of a Chinese passenger car, the working condition of a NEDC (network redundancy direct current) and the working condition of a HWFET (hardware-character field effect transistor), and obtaining the change results of the working states of an engine and a battery of a hybrid power system along with the vehicle speed and the required power of the whole vehicle, wherein the method specifically comprises:
the working condition of a Chinese passenger car, the working condition of a NEDC and the working condition of a HWFET are respectively dispersed into N different stages, 1 second is usually taken as one stage, and the required power of the whole car is as follows:
Preq＝η·(Pe+Pbat)
in the formula, PreqPower demand of the whole vehicle, η mechanical efficiency of the drive train, PeFor engine power, PbatIs the battery power; the working point of the engine is adjusted by controlling the power of the battery, and the working point of the engine is the maximum working point of the engineDetermining a superior working curve;
the battery power PbatAs a control variable u in global optimization controlkUsing the battery SOC as a state variable x in the global optimization controlk；
Establishing a global optimization target control model, wherein the global optimization target control model comprises a global optimization target function and constraint conditions, and the dynamic programming algorithm is adopted to meet the optimal control quantity of an optimization target;
the global optimization objective function is:
Figure GDA0002622281620000021
in the formula, mu is a weight coefficient and the value range is 0-1; cE(xk,uk) For fuel consumption costs, including engine fuel consumption and battery equivalent fuel consumption, CH(xk,uk) Cost is attenuated for battery life; caIs the conversion coefficient;
the k time fuel consumption cost CE(xk,uk) Comprises the following steps:
CE(xk,uk)＝Wfuel(k)+αPbat(k)
in the formula, Wfuel(k) For the fuel consumption of the engine at time k, determined by the operating point of the engine, Pbat(k) Battery power at time k, α is the equivalent fuel factor;
the k-time battery life decay cost CH(xk,uk) The following formula is used to obtain:
CH(xk,uk)＝σ·|Ic(k)|
where σ is a battery life decay influence factor, Ic(k) The battery charge-discharge multiplying power at the moment k;
the battery life attenuation influence factor sigma is calculated by the following formula:
Figure GDA0002622281620000031
where τ is the rated Life of the battery, i.e., the total amount of electricity flowing through the battery at the End of Life (EOL) under rated operating conditions; gamma is the total amount of charge flowing through the battery at the end of its life under actual operating conditions; i isc,nomThe rated charge-discharge rate of the battery;
the optimization sequence of the dynamic programming is as follows:
the cost function for the nth stage is:
JN *(xN i)＝min[L(xN i,uN j)]
the cost function at stage k is:
Jk *(xk i)＝min[L(xk i,uk j)+Jk+1 *(xk+1)]
where superscript i is an index of a discrete state variable, superscript j is an index of a discrete control variable, subscript k is an index of a discrete time, xk+1＝Sg[xk,uk]，Sg[xk,uk]Is a state transfer function, expressed as:
Figure GDA0002622281620000032
in the formula, SOCkFor the battery SOC at time k, Ik+1Current flowing through the battery at time k +1, QbatIs the battery capacity;
the constraint conditions are as follows:
Pe_min≤Pe(k)≤Pe_max
Pbat_min≤Pbat(k)≤Pbat_max
ωm_min≤ωm(k)≤ωm_max
Tm_min(ωm)≤Tm(k)≤Tm_max(ωm)
in the formula, Pe(k) Is k atEtching the power of the engine; pe_minAt minimum power of the engine, Pe_maxIs the maximum power of the engine, Pbat_minIs the minimum power of the battery, Pbat_maxIs the maximum power of the battery, omegam(k) Motor speed at time k, ωm_minAt minimum motor speed, ωm_maxAt the maximum rotational speed of the motor, Tm(k) Motor torque at time k, Tm_min(ωm) Is the minimum torque corresponding to the current rotating speed of the motor, Tm_max(ωm) The maximum torque is the maximum torque corresponding to the current rotating speed of the motor;
based on the established global optimization target control model, a dynamic programming problem is solved reversely, calculation is carried out from back to front from time k to N, the optimal decision track, the optimal state track and the optimal cost value of each stage are obtained gradually, and the solution is finished until k is 1;
fourthly, respectively solving the optimal decision track, the optimal state track and the optimal cost value of each stage obtained by the dynamic planning reverse calculation in a forward direction to obtain the optimal results of the working states of the engine and the battery of the hybrid power system along with the vehicle speed and the required power of the whole vehicle under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition;
(2) based on the optimization results of the working states of the engine and the battery of the hybrid power system under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET, along with the vehicle speed and the required power of the whole vehicle, obtained by the global optimization control, the battery life attenuation modes under all the working conditions are classified, and the method specifically comprises the following steps:
based on the global optimization result under each working condition, 13 characteristic parameters are selected for analysis, and the method comprises the following steps: average vehicle speed, maximum acceleration, acceleration average value, maximum deceleration, deceleration average value, battery charge-discharge rate maximum value, battery charge-discharge rate minimum value, proportion of battery charge-discharge rate between 0 and 3.5C, proportion of battery charge-discharge rate between 3.5C and 7C, proportion of battery charge-discharge rate above 7C, average power of an engine and average power of a battery;
dividing the global optimization results under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition respectively according to the identification period delta T by adopting a composite equal division method to obtain corresponding working condition blocks, and taking the working condition section between the middle points of the adjacent working condition blocks as a working condition to respectively extract the characteristic parameters of the global optimization results corresponding to each working condition block;
obtaining different battery life attenuation modes according to the change conditions of different battery charge-discharge multiplying power proportions based on the characteristic parameters of each working condition block obtained through calculation, wherein the battery life attenuation modes comprise a low multiplying power attenuation mode, a medium multiplying power attenuation mode and a high multiplying power attenuation mode, the low multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power between 0 and 3.5C accounts for the main proportion, the medium multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power between 3.5C and 7C accounts for the main proportion, and the high multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power above 7C accounts for the main proportion;
(3) according to the classification result of the battery life attenuation mode, a mean value clustering algorithm is adopted to uniformly select proper training samples to train a neural network model, and the method specifically comprises the following steps:
combining working condition block characteristic parameters corresponding to different battery life attenuation modes under various working conditions into corresponding working condition block sample sets, specifically, combining the working condition block characteristic parameters belonging to a low-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set, combining the working condition block characteristic parameters belonging to a medium-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set, and combining the working condition block characteristic parameters belonging to a high-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set;
forming training samples by using working condition block sample sets corresponding to different battery life attenuation modes, training a neural network model, and forming a mapping relation between the driving working condition characteristics and the working condition characteristics corresponding to the battery life attenuation modes under typical driving working conditions;
(4) based on a neural network model obtained by training, identifying a battery life attenuation mode under an actual driving working condition, and establishing a control strategy which can be applied to an actual vehicle on line under a corresponding mode, wherein the control strategy specifically comprises the following steps:
firstly, under the actual running condition, initial data acquisition is carried out, vehicle running data in an initial delay time period is collected, the initial identification delay time is delta P, and identification of a battery life decay mode is not carried out in a [0, delta P ] time period. When the time delta P is reached, the data processing module processes data stored in the delta P time period, extracts corresponding characteristic parameters according to the calculation rule of the characteristic parameters, and identifies by using a trained neural network model to determine the type of the battery life attenuation mode;
secondly, identifying the type of the battery life attenuation mode by using the similarity degree of characteristic parameters in a time segment [ t-delta P, t ] before the current time t and working condition characteristic parameters corresponding to the battery life attenuation mode under a typical driving working condition and a trained neural network model, taking the type as the battery life attenuation mode in a sampling step length [ t, t + delta f ] after the time t, updating the identification result every other sampling step length delta f, and generally taking delta f as 1 second;
and thirdly, extracting switching rules of the battery life attenuation modes according to global optimization results under each typical running condition, formulating control strategies corresponding to different battery life attenuation modes, and further determining the relation among the engine working point, the battery required power and the running state of the whole vehicle under different battery life attenuation modes, thereby determining throttle control and motor control in the real vehicle.
Compared with the prior art, the invention has the following advantages:
(1) the hybrid electric vehicle energy optimization method based on the battery life attenuation pattern recognition coordinately controls the fuel consumption cost and the battery life attenuation cost, and effectively slows down the attenuation degree of the battery life while ensuring that the fuel consumption of the whole vehicle is not changed greatly;
(2) according to the hybrid electric vehicle energy optimization method based on battery life attenuation mode recognition, infinite complex variable working conditions are converted into limited battery life attenuation modes, the accuracy of the system for predicting the battery life under the actual driving working condition is improved, and the adaptability of the control system to the actual driving working condition is effectively improved;
(3) the hybrid electric vehicle energy optimization method based on the battery life attenuation pattern recognition realizes the online real-time application of the real vehicle under the actual driving working condition, and has working condition universality;
(4) the hybrid electric vehicle energy optimization method based on the battery life attenuation mode recognition effectively improves the battery use performance, reasonably plans and utilizes the battery, and further reduces the vehicle use cost.
Drawings
The invention is further described with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow diagram of the overall process of the present invention;
FIG. 2 is a flow chart of control rules for classifying and extracting different battery life attenuation modes under various typical cycle conditions;
FIG. 3 is a flow chart of the battery life decay pattern recognition and real-time vehicle on-line real-time control under actual driving conditions.
Detailed Description
The invention is described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the invention discloses a hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition, which comprises the steps of firstly, respectively formulating energy management optimization control strategies under the working conditions of a Chinese passenger vehicle, a NEDC working condition and a HWFET working condition, taking the sum of fuel consumption cost and battery life attenuation cost of each stage as an optimization objective function, and solving by using a discrete dynamic programming algorithm; then, 13 characteristic parameters are selected, working condition characteristics and battery service life characteristics are covered, the global optimization results under all working conditions are equally divided according to the identification period delta T to obtain corresponding working condition blocks, the working condition section between the middle points of the adjacent working condition blocks is used as a working condition, and the 13 characteristic parameters of the global optimization results corresponding to all the working condition blocks are respectively extracted; classifying the battery life attenuation modes by using the characteristic parameters obtained by calculation, and extracting switching rules corresponding to different battery life attenuation modes; the method comprises the steps of selecting a proper training sample based on a mean value clustering algorithm, training a neural network model, collecting working condition data of a real vehicle under an actual driving working condition, identifying a battery life attenuation mode based on the neural network model, switching a control strategy under the corresponding battery life attenuation mode in real time, and further realizing on-line real-time control of the real vehicle. The energy optimization management control idea of the hybrid electric vehicle based on the battery life attenuation mode identification is concretely explained next.
(1) The method comprises the following steps of respectively carrying out global optimization control based on a dynamic programming algorithm aiming at the working condition of a Chinese passenger car, the working condition of a NEDC (network redundancy direct current) and the working condition of a HWFET (hardware-character field effect transistor), and obtaining the change results of the working states of an engine and a battery of a hybrid power system along with the vehicle speed and the required power of the whole vehicle, wherein the method specifically comprises:
the working condition of a Chinese passenger car, the working condition of a NEDC and the working condition of a HWFET are respectively dispersed into N different stages, 1 second is usually taken as one stage, and the required power of the whole car is as follows:
Preq＝η·(Pe+Pbat)
in the formula, PreqPower demand of the whole vehicle, η mechanical efficiency of the drive train, PeFor engine power, PbatIs the battery power; the working point of the engine is adjusted by controlling the power of the battery, and the working point of the engine is determined by the optimal working curve of the engine;
the battery power PbatAs a control variable u in global optimization controlkUsing the battery SOC as a state variable x in the global optimization controlk；
Establishing a global optimization target control model, wherein the global optimization target control model comprises a global optimization target function and constraint conditions, and the dynamic programming algorithm is adopted to meet the optimal control quantity of an optimization target;
the global optimization objective function is:
Figure GDA0002622281620000071
in the formula, mu is a weight coefficient and the value range is 0-1; cE(xk,uk) For fuel consumption costs, including engine fuel consumption and battery equivalent fuel consumption, CH(xk,uk) Cost is attenuated for battery life; caIs the conversion coefficient;
the k time fuel consumption cost CE(xk,uk) Comprises the following steps:
CE(xk,uk)＝Wfuel(k)+αPbat(k)
in the formula, Wfuel(k) For the fuel consumption of the engine at time k, determined by the operating point of the engine, Pbat(k) Battery power at time k, α is the equivalent fuel factor;
the k-time battery life decay cost CH(xk,uk) The following formula is used to obtain:
CH(xk,uk)＝σ·|Ic(k)|
where σ is a battery life decay influence factor, Ic(k) The battery charge-discharge multiplying power at the moment k;
the battery life attenuation influence factor sigma is calculated by the following formula:
Figure GDA0002622281620000072
wherein τ is the rated Life of the battery, i.e. the total amount of electricity flowing through the battery at the End of Life (EOL) under rated operating conditions, and the End of Life of the battery is considered when the actual capacity of the battery is reduced by 20% from the rated value; gamma is the actual operating condition (different discharge rates I)cTemperature θ, battery state of charge SOC) the total amount of electricity that flows through the battery at the end of its life; i isc,nomThe rated charge-discharge rate of the battery;
according to the optimization principle of dynamic programming, converting the optimal solution of global optimization control into the following optimization sequence problem:
the cost function for the nth stage is:
JN *(xN i)＝min[L(xN i,uN j)]
the cost function at stage k is:
Jk *(xk i)＝min[L(xk i,uk j)+Jk+1 *(xk+1)]
where superscript i is an index of a discrete state variable, superscript j is an index of a discrete control variable, subscript k is an index of a discrete time, xk+1＝Sg[xk,uk]，Sg[xk,uk]Is a state transfer function, expressed as:
Figure GDA0002622281620000081
in the formula, SOCkFor the battery SOC at time k, Ik+1Current flowing through the battery at time k +1, QbatIs the battery capacity;
the constraint conditions are as follows:
Pe_min≤Pe(k)≤Pe_max
Pbat_min≤Pbat(k)≤Pbat_max
ωm_min≤ωm(k)≤ωm_max
Tm_min(ωm)≤Tm(k)≤Tm_max(ωm)
in the formula, Pe(k) Engine power at time k; pe_minAt minimum power of the engine, Pe_maxIs the maximum power of the engine, Pbat_minIs the minimum power of the battery, Pbat_maxIs the maximum power of the battery, omegam(k) Motor speed at time k, ωm_minAt minimum motor speed, ωm_maxAt the maximum rotational speed of the motor, Tm(k) Is time kMotor torque, Tm_min(ωm) Is the minimum torque corresponding to the current rotating speed of the motor, Tm_max(ωm) The maximum torque is the maximum torque corresponding to the current rotating speed of the motor;
adding SOC boundary constraint based on stationary point detection:
SOClow(h,k)＝max(SOCmin,(SOClow(h,k+1)-max(I(h)/Qbat/3600)))
SOChigh(h,k)＝min(SOCmax,(SOChigh(h,k+1)-min(I(h)/Qbat/3600)))
in the formula, the optimization of a cost function is carried out on a discrete control variable, namely the battery power; i (h) measuring the battery current for the h discrete value of the discrete control variable; SOClow(h, k) represents a battery SOC lower boundary value when the control variable corresponding to the k moment is taken as the h discrete value; SOChigh③, based on the established global optimization target control model, reversely solving the dynamic programming problem, starting calculation from back to front from the moment k-N, gradually solving the optimal decision track, the optimal state track and the optimal cost value of each stage until the solution is finished when the moment k-1;
forward calculation is carried out, namely the initial value of the SOC of the battery is known from the end of the first moment to the N moment, the optimal decision track, the optimal state track and the optimal cost value of each stage obtained by the dynamic planning reverse calculation are utilized, the optimal control quantity of each moment under the working condition of the Chinese passenger car, the NEDC working condition and the HWFET working condition is obtained through interpolation respectively, and then the optimization results of the working states of the engine and the battery of the hybrid power system along with the vehicle speed and the required power of the whole vehicle under each working condition are obtained;
(2) referring to fig. 2, based on the results of optimizing the operating states of the engine and the battery of the hybrid power system with the vehicle speed and the power demand of the entire vehicle under the working conditions of the chinese passenger vehicle, the NEDC working condition, and the HWFET working condition obtained by the global optimization control, the battery life attenuation modes under the respective working conditions are classified, and the method specifically includes:
based on the global optimization result under each working condition, 13 characteristic parameters are selected for analysis, and the method comprises the following steps: average vehicle speed, maximum acceleration, acceleration average value, maximum deceleration, deceleration average value, battery charge-discharge rate maximum value, battery charge-discharge rate minimum value, proportion of battery charge-discharge rate between 0 and 3.5C, proportion of battery charge-discharge rate between 3.5C and 7C, proportion of battery charge-discharge rate above 7C, average power of an engine and average power of a battery;
dividing the global optimization results under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition respectively according to the identification period delta T by adopting a composite equal division method to obtain corresponding working condition blocks, and taking the working condition section between the middle points of the adjacent working condition blocks as a working condition to respectively extract the characteristic parameters of the global optimization results corresponding to each working condition block;
obtaining different battery life attenuation modes according to the change conditions of different battery charge-discharge multiplying power proportions based on the characteristic parameters of each working condition block obtained through calculation, wherein the battery life attenuation modes comprise a low multiplying power attenuation mode, a medium multiplying power attenuation mode and a high multiplying power attenuation mode, the low multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power between 0 and 3.5C accounts for the main proportion, the medium multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power between 3.5C and 7C accounts for the main proportion, and the high multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power above 7C accounts for the main proportion;
(3) referring to fig. 3, according to the classification result of the battery life decay pattern, a mean value clustering algorithm is used to uniformly select suitable training samples to train a neural network model, which specifically includes:
combining working condition block characteristic parameters corresponding to different battery life attenuation modes under various working conditions into corresponding working condition block sample sets, specifically, combining the working condition block characteristic parameters belonging to a low-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set, combining the working condition block characteristic parameters belonging to a medium-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set, and combining the working condition block characteristic parameters belonging to a high-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set;
forming training samples by using working condition block sample sets corresponding to different battery life attenuation modes, training a neural network model, and forming a mapping relation between the driving working condition characteristics and the working condition characteristics corresponding to the battery life attenuation modes under typical driving working conditions;
(4) referring to fig. 3, based on the neural network model obtained by training, the battery life attenuation mode is identified under the actual driving condition, and a control strategy which can be applied to the actual vehicle on line under the corresponding mode is established, specifically including:
firstly, under the actual running condition, initial data acquisition is carried out, vehicle running data in an initial delay time period is collected, the initial identification delay time is delta P, the vehicle is set to work in a pure electric mode, and the battery life attenuation mode is not identified in the [0, delta P ] time period. When the time delta P is reached, the data processing module processes data stored in the delta P time period, extracts corresponding characteristic parameters according to the calculation rule of the characteristic parameters, and identifies by using a trained neural network model to determine the type of the battery life attenuation mode;
secondly, identifying the type of the battery life attenuation mode by using the similarity degree of characteristic parameters in a time segment [ t-delta P, t ] before the current time t and working condition characteristic parameters corresponding to the battery life attenuation mode under a typical driving working condition and a trained neural network model, taking the type as the battery life attenuation mode in a sampling step length [ t, t + delta f ] after the time t, updating the identification result every other sampling step length delta f, and generally taking delta f as 1 second;
and thirdly, extracting switching rules of the battery life attenuation modes according to the global optimization result, formulating control strategies corresponding to different battery life attenuation modes, and further determining the relation among the engine working point, the battery required power and the running state of the whole vehicle under different battery life attenuation modes, thereby determining throttle control and motor control in the real vehicle.

Claims (1)

1. A hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition is characterized by comprising the following steps:
(1) the method comprises the following steps of respectively carrying out global optimization control based on a dynamic programming algorithm aiming at the working condition of a Chinese passenger car, the working condition of a NEDC (network redundancy direct current) and the working condition of a HWFET (hardware-character field effect transistor), and obtaining the change results of the working states of an engine and a battery of a hybrid power system along with the vehicle speed and the required power of the whole vehicle, wherein the method specifically comprises:
the working condition of a Chinese passenger car, the working condition of a NEDC and the working condition of a HWFET are respectively dispersed into N different stages, 1 second is usually taken as one stage, and the required power of the whole car is as follows:
Preq＝η·(Pe+Pbat)
in the formula, PreqPower demand of the whole vehicle, η mechanical efficiency of the drive train, PeFor engine power, PbatIs the battery power; the working point of the engine is adjusted by controlling the power of the battery, and the working point of the engine is determined by the optimal working curve of the engine;
the battery power PbatAs a control variable u in global optimization controlkUsing the state of charge (SOC) of the battery as a state variable x in the global optimization controlk；
Establishing a global optimization target control model, wherein the global optimization target control model comprises a global optimization target function and constraint conditions, and the dynamic programming algorithm is adopted to meet the optimal control quantity of an optimization target;
the global optimization objective function is:
Figure FDA0002622281610000011
in the formula, mu is a weight coefficient and the value range is 0-1; cE(xk,uk) For fuel consumption costs, including engine fuel consumption and battery equivalent fuel consumption, CH(xk,uk) Cost is attenuated for battery life; caIs the conversion coefficient;
the k time fuel consumption cost CE(xk,uk) Comprises the following steps:
CE(xk,uk)＝Wfuel(k)+αPbat(k)
in the formula, Wfuel(k) For the fuel consumption of the engine at time k, determined by the operating point of the engine, Pbat(k) Battery power at time k, α is the equivalent fuel factor;
the k-time battery life decay cost CH(xk,uk) The following formula is used to obtain:
CH(xk,uk)＝σ·|Ic(k)|
where σ is a battery life decay influence factor, Ic(k) The battery charge-discharge multiplying power at the moment k;
the battery life attenuation influence factor sigma is calculated by the following formula:
Figure FDA0002622281610000021
where τ is the rated Life of the battery, i.e., the total amount of electricity flowing through the battery at the End of Life (EOL) under rated operating conditions; gamma is the total amount of charge flowing through the battery at the end of its life under actual operating conditions; i isc,nomThe rated charge-discharge rate of the battery;
the optimization sequence of the dynamic programming is as follows:
the cost function for the nth stage is:
JN *(xN i)＝min[L(xN i,uN j)]
the cost function at stage k is:
Jk *(xk i)＝min[L(xk i,uk j)+Jk+1 *(xk+1)]
where superscript i is an index of a discrete state variable, superscript j is an index of a discrete control variable, subscript k is an index of a discrete time, xk+1＝Sg[xk,uk]，Sg[xk,uk]Is a state transfer function, expressed as:
Figure FDA0002622281610000022
in the formula, SOCkFor the battery SOC at time k, Ik+1Current flowing through the battery at time k +1, QbatIs the battery capacity;
the constraint conditions are as follows:
Pe_min≤Pe(k)≤Pe_max
Pbat_min≤Pbat(k)≤Pbat_max
ωm_min≤ωm(k)≤ωm_max
Tm_min(ωm)≤Tm(k)≤Tm_max(ωm)
in the formula, Pe(k) Engine power at time k; pe_minAt minimum power of the engine, Pe_maxIs the maximum power of the engine, Pbat_minIs the minimum power of the battery, Pbat_maxIs the maximum power of the battery, omegam(k) Motor speed at time k, ωm_minAt minimum motor speed, ωm_maxAt the maximum rotational speed of the motor, Tm(k) Motor torque at time k, Tm_min(ωm) Is the minimum torque corresponding to the current rotating speed of the motor, Tm_max(ωm) The maximum torque is the maximum torque corresponding to the current rotating speed of the motor;
based on the established global optimization target control model, a dynamic programming problem is solved reversely, calculation is carried out from back to front from time k to N, the optimal decision track, the optimal state track and the optimal cost value of each stage are obtained gradually, and the solution is finished until k is 1;
fourthly, respectively solving the optimal decision track, the optimal state track and the optimal cost value of each stage obtained by the dynamic planning reverse calculation in a forward direction to obtain the optimal results of the working states of the engine and the battery of the hybrid power system along with the vehicle speed and the required power of the whole vehicle under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition;
(2) based on the optimization results of the working states of the engine and the battery of the hybrid power system under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET, along with the vehicle speed and the required power of the whole vehicle, obtained by the global optimization control, the battery life attenuation modes under all the working conditions are classified, and the method specifically comprises the following steps:
based on the global optimization result under each working condition, 13 characteristic parameters are selected for analysis, and the method comprises the following steps: average vehicle speed, maximum acceleration, acceleration average value, maximum deceleration, deceleration average value, battery charge-discharge rate maximum value, battery charge-discharge rate minimum value, proportion of battery charge-discharge rate between 0 and 3.5C, proportion of battery charge-discharge rate between 3.5C and 7C, proportion of battery charge-discharge rate above 7C, average power of an engine and average power of a battery;
dividing the global optimization results under the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition respectively according to the identification period delta T by adopting a composite equal division method to obtain corresponding working condition blocks, and taking the working condition section between the middle points of the adjacent working condition blocks as a working condition to respectively extract the characteristic parameters of the global optimization results corresponding to each working condition block;
obtaining different battery life attenuation modes according to the change conditions of different battery charge-discharge multiplying power proportions based on the characteristic parameters of each working condition block obtained through calculation, wherein the battery life attenuation modes comprise a low multiplying power attenuation mode, a medium multiplying power attenuation mode and a high multiplying power attenuation mode, the low multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power between 0 and 3.5C accounts for the main proportion, the medium multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power between 3.5C and 7C accounts for the main proportion, and the high multiplying power attenuation mode is divided when the proportion of the battery charge-discharge multiplying power above 7C accounts for the main proportion;
(3) according to the classification result of the battery life attenuation mode, a mean value clustering algorithm is adopted to uniformly select proper training samples to train a neural network model, and the method specifically comprises the following steps:
combining working condition block characteristic parameters corresponding to different battery life attenuation modes under various working conditions into corresponding working condition block sample sets, specifically, combining the working condition block characteristic parameters belonging to a low-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set, combining the working condition block characteristic parameters belonging to a medium-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set, and combining the working condition block characteristic parameters belonging to a high-rate attenuation mode in the working conditions of the Chinese passenger vehicle, the NEDC working condition and the HWFET working condition into a working condition block sample set;
forming training samples by using working condition block sample sets corresponding to different battery life attenuation modes, training a neural network model, and forming a mapping relation between the driving working condition characteristics and the working condition characteristics corresponding to the battery life attenuation modes under typical driving working conditions;
(4) based on a neural network model obtained by training, identifying a battery life attenuation mode under an actual driving working condition, and establishing a control strategy which can be applied to an actual vehicle on line under a corresponding mode, wherein the control strategy specifically comprises the following steps:
firstly, under the actual running condition, acquiring initial data, collecting vehicle running data in an initial delay time period, wherein the initial identification delay time is delta P, identifying a battery life attenuation mode in the [0, delta P ] time period, processing the data stored in the delta P time period by a data processing module after the time delta P is reached, extracting corresponding characteristic parameters according to a calculation rule of the characteristic parameters, identifying by using a trained neural network model, and determining the category of the battery life attenuation mode;
secondly, identifying the type of the battery life attenuation mode by using the similarity degree of characteristic parameters in a time segment [ t-delta P, t ] before the current time t and working condition characteristic parameters corresponding to the battery life attenuation mode under a typical driving working condition and a trained neural network model, taking the type as the battery life attenuation mode in a sampling step length [ t, t + delta f ] after the time t, updating the identification result every other sampling step length delta f, and generally taking delta f as 1 second;
and thirdly, extracting switching rules of the battery life attenuation modes according to the global optimization result, formulating control strategies corresponding to different battery life attenuation modes, and further determining the relation among the engine working point, the battery required power and the running state of the whole vehicle under different battery life attenuation modes, thereby determining throttle control and motor control in the real vehicle.
CN201911095653.1A 2019-11-11 2019-11-11 Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition Active CN110775043B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911095653.1A CN110775043B (en) 2019-11-11 2019-11-11 Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911095653.1A CN110775043B (en) 2019-11-11 2019-11-11 Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition

Publications (2)

Publication Number Publication Date
CN110775043A CN110775043A (en) 2020-02-11
CN110775043B true CN110775043B (en) 2020-09-29

Family

ID=69391105

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911095653.1A Active CN110775043B (en) 2019-11-11 2019-11-11 Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition

Country Status (1)

Country Link
CN (1) CN110775043B (en)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112180280A (en) * 2020-09-27 2021-01-05 吉林大学 Hybrid electric vehicle battery life optimization method considering battery health state
CN112327170B (en) * 2020-11-13 2023-04-28 中汽研（天津）汽车工程研究院有限公司 Power battery full-period residual life estimation method based on neural network
CN112319462B (en) * 2020-11-17 2022-05-13 河南科技大学 Energy management method for plug-in hybrid electric vehicle
CN112590763A (en) * 2020-12-23 2021-04-02 吉林大学 Planet series-parallel hybrid electric vehicle energy optimization method based on battery heat and aging
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
CN112926211A (en) * 2021-03-09 2021-06-08 吉林大学 Fuel cell automobile energy management optimization method based on working condition prediction
CN113043911A (en) * 2021-04-09 2021-06-29 上海海事大学 Hybrid power ship energy control method considering battery life attenuation and computer equipment
CN113246797B (en) * 2021-06-04 2023-05-12 广州小鹏汽车科技有限公司 Method and device for predicting service life of battery
CN113392471B (en) * 2021-06-30 2022-11-29 华南农业大学 Hybrid electric vehicle reducer load spectrum compiling method, medium and equipment
CN113884927B (en) * 2021-07-31 2023-06-02 重庆长安新能源汽车科技有限公司 Battery life active control method and system based on cloud big data

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4238875B2 (en) * 2006-02-10 2009-03-18 トヨタ自動車株式会社 Battery life evaluation system for hybrid vehicles
KR101459485B1 (en) * 2013-11-22 2014-11-20 현대자동차 주식회사 System and method for low voltage dc-dc converter output control of green car
US9559390B2 (en) * 2013-12-19 2017-01-31 Ford Global Technologies, Llc Battery degradation accumulation methods
US9428072B2 (en) * 2014-01-09 2016-08-30 Ford Global Technologies, Llc Method and system for extending battery life
JP6269097B2 (en) * 2014-01-21 2018-01-31 株式会社デンソー Electric vehicle control system
CN104627167B (en) * 2015-01-28 2017-02-22 同济大学 Hybrid vehicle energy managing method and system considering service life of battery
JP2017114312A (en) * 2015-12-24 2017-06-29 いすゞ自動車株式会社 Hybrid vehicle and control method therefor
CN107600065A (en) * 2016-07-12 2018-01-19 贵航青年莲花汽车有限公司 A kind of control method of finished based on the optimization of electrokinetic cell life-span
KR102452548B1 (en) * 2017-04-18 2022-10-07 현대자동차주식회사 Apparatus for determination battery degradation, system having the same and method thereof
EP3621846A1 (en) * 2017-05-12 2020-03-18 The Ohio State Innovation Foundation Real-time energy management strategy for hybrid electric vehicles with reduced battery aging
DE102017212659A1 (en) * 2017-07-24 2019-01-24 Audi Ag Method for operating an electrical overall on-board network, control unit and motor vehicle
CN107878445B (en) * 2017-11-06 2019-01-18 吉林大学 A kind of energy-optimised management method of hybrid vehicle considering cell performance decay
CN108418272B (en) * 2018-03-28 2020-05-08 吉林大学 Battery pack balancing system considering battery life and control method
CN110018423A (en) * 2019-05-07 2019-07-16 江苏吉意信息技术有限公司 Battery life Prediction System and battery life predictor method
CN110254418B (en) * 2019-06-28 2020-10-09 福州大学 Hybrid electric vehicle reinforcement learning energy management control method

Also Published As

Publication number Publication date
CN110775043A (en) 2020-02-11

Similar Documents

Publication Publication Date Title
CN110775043B (en) Hybrid electric vehicle energy optimization method based on battery life attenuation pattern recognition
CN110775065B (en) Hybrid electric vehicle battery life prediction method based on working condition recognition
CN110562239B (en) Variable-domain optimal energy management control method and device based on demand power prediction
CN108819934B (en) Power distribution control method of hybrid vehicle
CN108791270B (en) Power component working point regulation and control method of hybrid power vehicle based on working condition prediction
CN111619545B (en) Hybrid electric vehicle energy management method based on traffic information
CN110962837B (en) Plug-in hybrid electric vehicle energy management method considering driving style
CN112180280A (en) Hybrid electric vehicle battery life optimization method considering battery health state
CN109552338B (en) Evaluation method and system for ecological driving behavior of pure electric vehicle
CN103606271B (en) A kind of mixed power city bus control method
CN108909702A (en) A kind of plug-in hybrid-power automobile energy management method and system
CN112319462B (en) Energy management method for plug-in hybrid electric vehicle
CN110356397B (en) Hybrid electric vehicle optimization method based on energy normalization minimization of road gradient
CN112327168A (en) XGboost-based electric vehicle battery consumption prediction method
CN112937547A (en) Plug-in hybrid power bus energy management method based on global working conditions
CN110395245B (en) Hybrid electric vehicle energy management system based on fixed route driving information
CN113815437B (en) Predictive energy management method for fuel cell hybrid electric vehicle
CN111775925A (en) Working mode decision method and device for power split hybrid electric vehicle
CN111923897A (en) Intelligent energy management method for plug-in hybrid electric vehicle
CN113276829B (en) Vehicle running energy-saving optimization weight-changing method based on working condition prediction
CN110356396B (en) Method for instantaneously optimizing speed of electric vehicle by considering road gradient
CN110077389B (en) Energy management method for plug-in hybrid electric vehicle
CN112590763A (en) Planet series-parallel hybrid electric vehicle energy optimization method based on battery heat and aging
CN116702996A (en) Electric bus energy prediction method based on typical working condition recognition
CN114655074B (en) Electric vehicle actual running energy consumption estimation method based on Bayesian regression

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant