CN117034666B - Fuel cell automobile energy management method, system, medium, equipment and terminal - Google Patents
Fuel cell automobile energy management method, system, medium, equipment and terminal Download PDFInfo
- Publication number
- CN117034666B CN117034666B CN202311307416.3A CN202311307416A CN117034666B CN 117034666 B CN117034666 B CN 117034666B CN 202311307416 A CN202311307416 A CN 202311307416A CN 117034666 B CN117034666 B CN 117034666B
- Authority
- CN
- China
- Prior art keywords
- fuel cell
- cost
- power
- battery
- model
- 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
Links
- 239000000446 fuel Substances 0.000 title claims abstract description 182
- 238000007726 management method Methods 0.000 title claims description 57
- 239000001257 hydrogen Substances 0.000 claims abstract description 55
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 55
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims abstract description 53
- 238000000034 method Methods 0.000 claims abstract description 41
- 238000012423 maintenance Methods 0.000 claims abstract description 39
- 238000005457 optimization Methods 0.000 claims abstract description 36
- 230000007423 decrease Effects 0.000 claims abstract description 22
- 238000005096 rolling process Methods 0.000 claims abstract description 10
- 238000005070 sampling Methods 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 40
- 230000032677 cell aging Effects 0.000 claims description 29
- 230000032683 aging Effects 0.000 claims description 26
- 230000036541 health Effects 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 16
- 230000000694 effects Effects 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 150000002431 hydrogen Chemical class 0.000 claims description 3
- 229910052744 lithium Inorganic materials 0.000 claims description 3
- 238000013439 planning Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000001052 transient effect Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 description 9
- 230000008901 benefit Effects 0.000 description 7
- 238000005265 energy consumption Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000001737 promoting effect Effects 0.000 description 3
- 238000012952 Resampling Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
-
- 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
- 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
- B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
-
- 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
- B60L58/40—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for controlling a combination of batteries and fuel cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- 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
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/40—Application of hydrogen technology to transportation, e.g. using fuel cells
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Sustainable Development (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Mechanical Engineering (AREA)
- Transportation (AREA)
- Business, Economics & Management (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Automation & Control Theory (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Public Health (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Fuel Cell (AREA)
Abstract
The invention belongs to the technical field of control of a power system of a fuel cell automobile, and discloses a method, a system, a medium, equipment and a terminal for managing energy of the fuel cell automobile, wherein a full life cycle operation and maintenance cost model is established by combining the voltage decline degree of a fuel cell and the capacity decline degree of the power cell and the hydrogen consumption cost; using the full life cycle operation and maintenance cost as an objective function of model predictive control; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationBattery state of charge, SOC, and fuel cell output power as control variables for the systemIs a state variable; solving the above-established constraint-containing optimization problem using sequence quadratic programming in each prediction time domain, deriving an optimal control sequence, and applying the calculated first value of the optimal control sequence toAnd the model is used for sampling the vehicle state again, solving a new optimization problem and realizing rolling optimization.
Description
Technical Field
The invention belongs to the technical field of control of a power system of a fuel cell automobile, and particularly relates to a method, a system, a medium, equipment and a terminal for managing energy of the fuel cell automobile.
Background
At present, china is promoting the transformation of traffic electrification, the fuel cell automobile has the inherent advantages of high efficiency and zero emission, and the short plates of the pure electric automobile in the aspects of energy density, charging time, low-temperature performance and the like can be overcome, so that the fuel cell automobile becomes a research hot spot in the global scope. Fuel cell automobiles typically employ a hybrid drive mode, i.e., adding a power cell or super capacitor as an auxiliary power source on the basis of having a fuel cell. Depending on an advanced energy management system, the hydrogen fuel cell bus can be normally started to operate under the extremely cold weather condition of minus 35 ℃, has the endurance mileage of 450 km and can finish the rapid filling of hydrogen fuel within 12 minutes. However, the high operation and maintenance costs severely restrict the commercialization process of the hydrogen fuel cell car, and how to effectively reduce the operation and maintenance costs of the whole car is a difficult problem to be solved in accelerating the commercialization process of the hydrogen fuel cell car.
As a backbone of the overall vehicle control system, an energy management strategy monitors the operating state of various components within the power system and distributes the vehicle load power demand in real-time among the multiple power sources. Advanced energy management strategies can reduce system energy consumption and increase critical component life while preserving vehicle dynamics. In the existing energy management technology at present, for example, patent number is CN116101130B, a method and a system for managing energy of a fuel cell automobile are disclosed, energy distribution is realized from a global angle according to the whole power requirement of a whole driving period from a starting point to an ending point of the automobile, energy optimal distribution is realized through a traversing algorithm, and global optimization with minimum energy consumption is realized. Although the method can realize global optimization of economy, the service life cost of the power battery and the fuel battery is not considered in an objective function, and the method cannot be applied in real time. Patent number CN116278993A, a fuel cell automobile energy management control method considering multi-objective optimization converts the service life cost of a power cell and the service life cost of the fuel cell into equivalent hydrogen consumption, establishes the multi-objective optimization problem by adopting the Pontrian Jin Jixiao value principle, but the establishment process of the multi-objective optimization method involves subjective weight selection and can influence the optimality of the result.
Through the above analysis, the problems and defects existing in the prior art are as follows: most of the existing energy management technologies aim at minimum energy consumption, the service life of a power system cannot be prolonged, and few energy management technologies considering the service life cost of the power system still have an optimization space in the aspect of reducing the operation and maintenance cost of a vehicle.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a fuel cell automobile energy management method, a system, a medium, equipment and a terminal.
The invention is realized in such a way, and the energy management method of the fuel cell automobile is characterized in that the aging cost of a power source is quantified according to the voltage decline degree and the capacity decline degree of the power cell, and a full life cycle operation and maintenance cost model is established by combining the hydrogen consumption cost; using the full life cycle operation and maintenance cost as an objective function of model predictive control; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationAs control variables of the system, the state of charge SOC of the battery and the fuel cell output power +.>Is a state variable; solving the established optimization problem containing the constraint by using sequence quadratic programming in each prediction time domain, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, forward rolling the prediction time domain, and repeating the process to realize real-time energy management in all driving periods.
Further, the fuel cell automobile energy management method specifically includes the following steps:
step one: establishing a health model of the fuel cell and the power cell, and representing the aging degree of the fuel cell and the power cell by the voltage decline degree and the capacity decline degree of the fuel cell respectively to quantify the aging cost of the fuel cell and the power cell; combining the hydrogen consumption cost, and establishing a full life cycle operation and maintenance cost model;
step two: establishing an objective function and constraint conditions of a model predictive control problem, and taking full life cycle operation and maintenance cost as the objective function of the model predictive control, wherein the objective function comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost; the cost is directly used as an objective function, so that the subjective setting of the weights of multiple objective functions is avoided; and then determining constraint conditions according to the normal operation range of each component of the power system. Selecting fuel cell output power variationBattery state of charge +.>And fuel cell output +.>Establishing a model predictive control problem for the state variables;
step three: in each prediction time domainSolving the established optimization problem containing constraints by using sequence quadratic programmingAnd (3) deriving an optimal control sequence, applying a first value of the calculated optimal control sequence to the model, pushing the prediction view forward further, solving the optimization problem again in the new prediction view, realizing rolling optimization of model prediction control, and completing real-time application of the energy management method in a period of driving.
Further, the first step specifically includes:
(1a) Fuel cell aging cost:
fuel cell aging is caused by four conditions of start-stop, load change, low load and high load,and->Maximum value of fuel cell output power and allowable output of fuel cell output power, respectively, +.>Defined as a high load, which is defined as a high load,defined as low load, +.>For the fuel cell output power variation, +.>Defining as one-time variable load; by start-stop cost->Load-changing cost->Low cost->And high load costs->Direct addition to obtain predicted time domain fuel cellAging cost->：/>；
;
;
;
;
Wherein the method comprises the steps ofThe method is used for predicting the times of starting, stopping and changing load of the fuel cell in the time domain respectively>Andthe duration of low and high load of the fuel cell in the time domain is predicted by a period of time, respectively>、/>、/>Andthe aging coefficients of fuel cell start-stop, load-change, low load and high load, respectively,/->For fuel cell price, the fuel cell voltage drop reaches 10% of rated voltage, namely the life end is considered;
(1b) Power battery aging cost:
usingRepresenting the predicted aging cost of the power battery in the time domain:;the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A battery price of 6.5 Ah;representation->The state of battery health at the moment; />The battery health state at the time k is represented;is->Battery current at time; ->Refers to discharge rate of +.>Life cycle number of the battery;is battery capacity;>for sampling time interval>Is the prediction time domain of model prediction control;
(1c) Cost of hydrogen consumption:
usingRepresenting predicted hydrogen consumption costs for the fuel cell in the time domain:wherein, the->Is hydrogen cost;>is the hydrogen consumption rate;is the power supplied by hydrogen>Is the heating value of hydrogen; from a pre-fit of hydrogen power to fuel cell output powerIs obtained from the relationship curve of: />。
The full life cycle operation and maintenance cost is the sum of the fuel cell aging cost, the power cell aging cost and the hydrogen consumption cost.
Further, the second step specifically includes: prediction time domain5s, the control variable is the fuel cell output variation +.>The state variable is the state of charge of the batteryStatus->And fuel cell output +.>The method comprises the steps of carrying out a first treatment on the surface of the Using the full life cycle operation cost established in step one as an objective function, then +.>The multi-objective cost function in each prediction domain is:. Wherein->Representing a limitation on the battery terminal SoC,，/>is a larger positive number, +.>Is the SoC value at time k, +.>Is the desired SoC value after the end of the driving cycle.
The constraint conditions are as follows:
;
the above 5 constraints have the following effects: limiting fuel cell output power to a minimumMaximum->Between, i.e., 0-1200W; limiting the variation of the output power of the fuel cell to a minimum value +.>Maximum->Between, i.e., 0-300W; limiting the battery SoC to a minimum value +.>Maximum->Between, i.e., 0.6-0.8; initial value of battery state of charge +.>1 is shown in the specification; initial value of battery state of health +.>1 is shown in the specification;
wherein,are respectively->Fuel cell output power at a time, fuel cell output power variation amount, and battery SoC value.
Further, the third step specifically includes:
(3a) Setting an iteration initial value of a systemAnd convergence accuracy, setting k=0, representing iteration initiation;
(3b) Using Taylor expansion to bring the objective function to the iteration pointThe process is simplified into a secondary planning problem;
(3c) Solving a quadratic programming problem;
(3d) One-dimensional search is carried out on the cost function under the constraint condition to obtain the next iteration point；
(3e) If at the point of iterationSatisfying the termination rule ∈>Is regarded as the optimal result, < >>The optimal cost is considered as the objective function and the calculation process is terminated; otherwise, the next step is carried out;
(3f) Setting k=k+1, returning to (3 b), and repeating the above steps.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the fuel cell vehicle energy management method.
Another object of the present invention is to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the fuel cell vehicle energy management method.
Another object of the present invention is to provide an information data processing terminal for implementing the fuel cell vehicle energy management method.
Another object of the present invention is to provide a fuel cell vehicle energy management system based on the fuel cell vehicle energy management method, the fuel cell vehicle energy management system comprising:
the power system health model building module is used for representing the aging degree of the fuel cell voltage decline and the power cell capacity decline degree and building a full life cycle operation and maintenance cost model by combining the hydrogen consumption cost;
model predictive control module for building an energy management framework forThe full life cycle operation and maintenance cost is used as an objective function of model predictive control and comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationAs control variables of the system, the state of charge SOC of the battery and the fuel cell output power +.>Is a state variable;
and the control sequence solving module is used for solving the established constraint-containing optimization problem by using sequence quadratic programming in each prediction time domain, deducing an optimal control sequence, applying the calculated first value of the optimal control sequence to the model, pushing the prediction vision forward further, repeating the solving process, realizing the rolling optimization of the model prediction control, and completing the real-time application of the energy management technology in a period of driving period.
Another object of the present invention is to provide a fuel cell vehicle power system including the fuel cell vehicle energy management system; the back end of the fuel cell is connected with the DC/DC converter and connected with the bus loop, and the lithium cell is directly connected with the bus.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty in solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows: the fuel cell automobile energy management method based on model predictive control not only can realize the optimization of the traditional energy management method on the hydrogen consumption cost, but also can realize the extension of the service lives of a fuel cell and a power cell and the reduction of the operation and maintenance cost of the whole life cycle of a power source; the model predictive control strategy has stronger capability of processing constraint-containing multi-element systems and potential of real-time application, and the control effect is outstanding; the nonlinear energy management problem is solved by using sequence quadratic programming, so that the algorithm execution efficiency is high, and the method can be applied in real time.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages: the invention provides a fuel cell automobile energy management strategy which is based on model predictive control and is oriented to optimizing operation and maintenance costs, and aims to overcome the defect of the existing energy management technology in the aspect of reducing the operation and maintenance costs of the whole life cycle of an automobile. The aging cost of the fuel cell and the power cell is further quantified by establishing a health model of the fuel cell and the power cell, a full life cycle operation and maintenance cost model is established, and the operation and maintenance cost is taken as an objective function to be incorporated into a model predictive control energy management framework, so that the service life of the power system can be prolonged while the running cost of the vehicle is reduced, and the method has important value and significance in promoting the commercialization process of the hydrogen fuel cell automobile.
Thirdly, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
(1) The expected benefits and commercial values after the technical scheme of the invention is converted are as follows:
the technological and industrial chain of "storage and transportation" technology of hydrogen energy has been advanced to the national strategic level. Compared with the traditional fuel oil vehicle, the hydrogen fuel cell vehicle has the inherent advantages of high efficiency and zero emission, and can overcome the shortages of the pure electric vehicle in the aspects of energy density, charging time, low-temperature performance and the like, and the hydrogen fuel cell vehicle becomes a research hot spot facing the future long-time endurance and all-weather transportation field in the global scope, but the high running and maintenance cost restricts the large-scale commercial application of the hydrogen fuel cell vehicle. The advanced energy management technology can improve the working efficiency and service life of the power system so as to reduce the running and maintenance cost of the vehicle, and is a core technical bottleneck which is urgent to break through and is required to be broken through in the commercialization process of the fuel cell automobile. The invention aims to break through the core application support technical bottleneck of the energy management of the fuel cell automobile, provides solid theoretical support for further reducing the running cost of the automobile and prolonging the service life of a power system, has important application value and significance for accelerating the commercialization process of the hydrogen fuel cell automobile, and further, the research result of the invention is a core element breaking through the bottleneck of the full industrial chain of hydrogen energy transportation, and has important strategic significance for enhancing the conversion of the energy structure in China.
(2) The technical scheme of the invention fills the technical blank in the domestic and foreign industries:
fuel cell vehicle energy management is a multi-objective optimization problem, and various factors need to be considered, besides direct energy consumption, the aging of fuel cells and power cells can indirectly lead to cost improvement, and most of existing strategies are not fully considered. Secondly, the existing research does not build an accurate model for the aging mechanism of the fuel cell and the power cell, which makes it difficult to describe the aging trend of the energy components and to realize the cooperative optimization of economy and durability. The invention designs an energy management strategy of the fuel cell automobile, which is capable of improving the economical efficiency of the automobile, improving the durability of the fuel cell and prolonging the service life of the battery, and fills the gap of the energy management technology of optimizing the operation and maintenance costs of the whole life cycle at home and abroad.
Drawings
FIG. 1 is a flow chart of a fuel cell vehicle energy management method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a power system topology according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a fuel cell vehicle energy management method according to an embodiment of the present invention.
Description of the embodiments
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the method for managing energy of a fuel cell vehicle according to the embodiment of the invention includes the following steps:
s101: characterizing the aging degree of the fuel cell voltage decay and the power cell capacity decay degree by combining the hydrogen consumption cost to establish a full life cycle operation and maintenance cost model;
s102: the full life cycle operation and maintenance cost is used as an objective function of model predictive control, and the objective function comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationAs control variables of the system, the state of charge SOC of the battery and the fuel cell output power +.>Is a state variable;
s103: solving the established optimization problem containing the constraint by using sequence quadratic programming in each prediction time domain, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, pushing forward a prediction view field further, resampling the state of the vehicle at the moment, solving a new optimization problem, realizing rolling optimization of model prediction control, and completing real-time application of the energy management method in a period of driving cycle.
The method for managing the energy of the fuel cell automobile provided by the embodiment of the invention specifically comprises the following steps:
step one: and establishing a health model of the fuel cell and the power cell, and representing the aging degree of the fuel cell and the power cell by the voltage decline degree and the capacity decline degree of the fuel cell respectively to further quantify the aging cost of the fuel cell and the power cell. And combining the hydrogen consumption cost to establish a full life cycle operation and maintenance cost model.
Step two: and establishing an objective function and constraint conditions of the model predictive control problem. The full life cycle operation and maintenance cost is used as an objective function of model predictive control, and comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost. This directly takes the cost as an objective functionThe method is used for avoiding subjective setting of the weight of the multi-objective function and improving control accuracy. And then determining constraint conditions according to the normal operation range of each component of the power system. Selecting fuel cell output power variationBattery state of charge +.>And fuel cell output +.>Is a state variable.
Step three: in each prediction time domainSolving the established optimization problem containing the constraint by using sequence quadratic programming, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, pushing the prediction view forward further, solving the optimization problem again in the new prediction view, realizing rolling optimization of model prediction control, and completing real-time application of the energy management method in a driving period.
The topology of the power system of this embodiment is shown in fig. 2, where the back end of the fuel cell is connected to the DC/DC converter and then connected to the bus loop, and the lithium battery is directly connected to the bus. The power battery is a non-plug-in type fuel battery with the capacity of 65Ah, the fuel battery is a proton exchange membrane fuel battery, and the allowable output maximum value of the output power of the fuel battery1.2kW and the vehicle mass 578kg.
As shown in fig. 3, the energy management method of the example of the present invention includes the following three steps:
step one, a full life cycle operation and maintenance cost model is established:
(1a) Fuel cell aging cost：
Fuel cell aging is mainly caused by four conditions of start-stop, load change, low load and high load,defined as high load, +.>Defined as low load, transient power change of fuel cell between two samplingsDefined as one time variable load->Is the maximum power of the fuel cell. By start-stop cost->Load-changing cost->Low cost->And high load costs->Direct addition to obtain predicted time domain fuel cell aging cost：/>. In the middle ofThe method comprises the following steps of:
；
；
；
；
wherein the method comprises the steps ofThe method is used for predicting the times of starting, stopping and changing load of the fuel cell in the time domain respectively>Andthe method comprises the steps of predicting duration time of low load and high load of the fuel cell in a time domain respectively; />Andis the aging coefficient of the fuel cell with the functions of start-stop, load changing, low load and high load; />For fuel cell price, the fuel cell voltage drop reaches 10% of rated voltage, which is considered end of life.
(1b) Power battery aging cost：
UsingRepresenting the predicted aging cost of the power battery in the time domain:；。
in the method, in the process of the invention,a battery price of 6.5 Ah; />Representation->Battery state of health at time->Representation->Battery current at time, ">Refers to discharge rate of +.>Cycle number of life cycle of battery, +.>Is the battery capacity.
(1c) Cost of hydrogen consumption：
UsingRepresenting predicted hydrogen consumption costs for the fuel cell in the time domain:. Wherein (1)>Is hydrogen cost->Is the rate of consumption of hydrogen gas,is the heating value of hydrogen. />Is the power provided by hydrogen, and is formed by fitting the hydrogen power and the output power of the fuel cell in advanceIs obtained from the relationship curve of: />；
Because the power battery of the embodiment is non-plug-in, the SoC of the battery in the beginning and end states should be ensured to be similar as much as possible,representing a limitation on the battery terminal SoC: />。
Wherein,，/>for battery charge and discharge efficiency, < >>Charge and discharge current for battery, ">Is battery capacity,/->Is a larger positive number.
The full life cycle operation and maintenance cost is obtained by adding the four items.
Step two, establishing a model predictive control framework
In this embodiment, the prediction time domain5s, the control variable is a fuel cellOutput power variation->The state variable is battery state of charge +.>And fuel cell output +.>. And (3) using the full life cycle cost established in the second step as an objective function, and reducing uncertainty caused by subjective weight selection. The multi-objective cost function in the kth prediction domain is:
；
wherein,representing restrictions on the battery terminal SoC +.>，/>Is a larger positive number, +.>Is the SoC value at time k, +.>Is the desired SoC value after the end of the driving cycle.
The constraint conditions are as follows:
；
the above 5 constraints have the following effects: limiting fuel cell output power to a minimumMaximum->Between, i.e., 0-1200W; limiting the variation of the output power of the fuel cell to a minimum value +.>Maximum->Between, i.e., 0-300W; limiting the battery SoC to a minimum value +.>Maximum->Between, i.e., 0.6-0.8; initial value of battery state of charge +.>1 is shown in the specification; initial value of battery state of health +.>1.
Step three, solving sequence quadratic programming
Solving the multi-objective optimization problem with constraint conditions based on model predictive control established in the step two by using sequence quadratic programming, wherein the solving specifically comprises the following sub-steps:
(3a) Setting initial values of the systemAnd convergence accuracy, k=0;
(3b) Using Taylor expansion to bring the objective function to the iteration pointThe process is simplified into a secondary planning problem;
(3c) Solving a quadratic programming problem;
(3d) One-dimensional search is carried out on the cost function under the constraint condition to obtain the next iteration point；
(3e) If at the point of iterationSatisfying the termination rule ∈>Is regarded as the optimal result, < >>Is considered the optimal cost for the objective function and the calculation process terminates. Otherwise, the next step is carried out;
(3f) Setting k=k+1, returning to (3 b), and repeating the above steps.
And (3) acting the first item of the optimal control track obtained by solving the sequence quadratic programming on the controlled object, namely completing the optimal control in the primary prediction time domain. And then pushing the prediction vision forward further, repeating the third step, and continuously solving the optimization problem of the next prediction time domain, so that the rolling optimization of the model prediction control can be realized, and the real-time application of the energy management method in a driving period is completed.
The fuel cell automobile energy management system provided by the embodiment of the invention comprises:
and the health model building module is used for representing the aging degree of the fuel cell voltage decline degree and the power cell capacity decline degree by combining the hydrogen consumption cost and building a full life cycle operation and maintenance cost model.
The model prediction control module is used for taking the full life cycle operation and maintenance cost as an objective function of model prediction control and comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationAs control variables of the system, the state of charge SOC of the battery and the fuel cell output power +.>Is a state variable;
the control sequence deducing module is used for solving the established optimization problem containing the constraint by using sequence quadratic programming in each prediction time domain, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, pushing the prediction view range forward further, resampling the state of the vehicle at the moment, solving a new optimization problem, realizing rolling optimization of model prediction control, and completing real-time application of the energy management method in a driving period.
In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
The embodiment of the invention is applied to the fuel cell postal delivery vehicle. The invention can be applied to fuel cell heavy-duty vehicles such as fuel cell buses and fuel cell heavy trucks. The invention can carry out real-time energy management on heavy vehicles such as a fuel cell bus, a fuel cell heavy truck and the like, reduces the operation and maintenance cost of the vehicles, and has important value and significance for promoting the large-scale commercialization of the fuel cell heavy truck.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The invention is a simulation performed by MATLAB software on an operating system with a central processing unit of Intel (R) Core (TM) i 5-12400.5 GHz CPU and a memory 16G, WINDOWS. Under the same driving cycle, the control effect of the energy management strategy based on the rule is compared with that of the control effect of the energy management strategy based on the rule, and the driving cycle is acquired from an actual road and has the length of 15000s. The comparison results are shown in table 1, and the rule-based strategy does not consider fuel cell and cell aging costs, which can lead to power source aging and higher operating and maintenance costs. Thus, the invention has advantages in reducing hydrogen consumption and full life cycle operation and maintenance costs.
Table 1 shows the control effect of the present invention compared to a rule-based energy management strategy
| The invention is that | Rule-based policies | |
| Consumption hydrogen quality (g) | 92.1 | 14.7 |
| Operation and maintenance cost (Yuan) | 4.78 | 5.11 |
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (6)
1. The fuel cell automobile energy management method is characterized in that a full life cycle operation and maintenance cost model is established by combining hydrogen consumption cost according to the voltage decline degree of a fuel cell and the capacity decline degree of a power cell; using the full life cycle operation and maintenance cost as an objective function of model predictive control; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationBattery state of charge +.>And fuel cell output +.>Is a state variable; solving the established optimization problem containing the constraint in each prediction time domain by using sequence quadratic programming, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, and repeatedly establishing an objective function and constraint conditions of the model prediction control problem;
the energy management method of the fuel cell automobile specifically comprises the following steps:
step one: establishing a health model of the fuel cell and the power cell, and representing the aging degree of the fuel cell and the power cell according to the voltage decline degree and the capacity decline degree of the fuel cell respectively to further quantify the aging cost of the fuel cell and the power cell; combining the hydrogen consumption cost, and establishing a full life cycle operation and maintenance cost model;
step two: establishing an objective function and constraint conditions of a model predictive control problem; using allThe life cycle operation and maintenance cost is used as an objective function of model predictive control and comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost; the cost is directly used as an objective function, so that the subjective setting of the weights of multiple objective functions is avoided; then determining constraint conditions according to the normal operation range of each component of the power system; selecting fuel cell output power variationBattery state of charge +.>And fuel cell output +.>Is a state variable;
step three: in each prediction time domainSolving the established optimization problem containing the constraint by using sequence quadratic programming, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, pushing forward a prediction view field further, solving the optimization problem again in a new prediction view field, realizing rolling optimization of model prediction control, and completing real-time application of an energy management method in a driving period;
the first step specifically comprises the following steps:
(1a) Fuel cell aging cost:
fuel cell aging is caused by four conditions of start-stop, load change, low load and high load,defined as a high load, which is defined as a high load,defined as low load, +.>And->Fuel cell transient power variation between two samplings +.>Defined as one time variable load->Output power variation for the fuel cell; by start-stop cost->Load-changing cost->Low cost->And high load costs->Direct addition to obtain the predicted time domain fuel cell aging cost +.>：
；
；
；
；
；
Wherein,the method is characterized in that the method is used for predicting the number of times of starting, stopping and changing load of the fuel cell in the time domain respectively, and the time of starting, stopping and changing load of the fuel cell in the time domain is +.>And->The duration of low and high load of the fuel cell in the time domain is predicted, respectively,/->Is the aging coefficient of fuel cell start-stop, load-change, low load and high load, +.>For fuel cell price, the fuel cell voltage drop reaches 10% of rated voltage, namely the life end is considered;
(1b) Power battery aging cost:
usingRepresenting the predicted aging cost of the power battery in the time domain:
；
；
is the battery price of 6.5Ah, < >>Representation->Battery state of health at time->Representation ofBattery state of health at time->Is->Battery current at time, ">Is->The battery current at the moment in time,refers to discharge rate of +.>Cycle number of life cycle of battery at time discharge rate, +.>Is battery capacity,/->For sampling time interval, +.>Is the prediction time domain of model prediction control;
(1c) Cost of hydrogen consumption:
usingRepresenting predicted hydrogen consumption costs for the fuel cell in the time domain:
；
wherein,is hydrogen cost->Is the hydrogen consumption rate; />Is the power supplied by hydrogen, ">Is the heat value of hydrogen, which is a function of the hydrogen power and the fuel cell power which are fitted in advance>Is obtained from the relationship curve of:；
the full life cycle operation and maintenance cost is the sum of the fuel cell aging cost, the power cell aging cost and the hydrogen consumption cost;
the second step specifically comprises the following steps: prediction time domain5s, the control variable is the fuel cell output variation +.>The state variable is battery state of charge +.>And fuel cell output +.>The method comprises the steps of carrying out a first treatment on the surface of the Using the full life cycle cost established in step one as an objective function, then +.>The multi-objective cost function in each prediction domain is:
；
wherein,representing restrictions on the battery terminal SoC +.>，/>Is a larger positive number, +.>Is the SoC value at time k, +.>Is the expected SoC value after the driving period is finished; />Representing predicted hydrogen consumption costs of the fuel cell in the time domain; />Representing the predicted aging cost of the power battery in the time domain; />Representing a predicted time domain fuel cell aging cost;
the constraint conditions are as follows:
；
the above 5 constraints have the following effects: limiting the fuel cell output power to a maximum operating range, i.e., 0-1200W; limiting the variation of the output power of the fuel cell to 0-300W; limiting the battery SoC to between 0.6 and 0.8; the initial value of the charge state of the battery is 1; the initial value of the battery state of health is 1;
wherein,are respectively->Fuel cell output power, fuel cell power variation, battery SoC value at time; />And->Representing the minimum and maximum values of the allowable output of the fuel cell,and->A minimum value and a maximum value representing the variation amount of the output power of the fuel cell; />And->Representing minimum and maximum values of the battery SoC; />Representing an initial value of the state of charge of the battery,/->An initial value representing a state of health of the battery;
the third step specifically comprises the following steps:
(3a) Setting initial values of the systemAnd convergence accuracy, k=0;
(3b) Using Taylor expansion to bring the objective function to the iteration pointThe process is simplified into a secondary planning problem;
(3c) Solving a quadratic programming problem;
(3d) One-dimensional search is carried out on the cost function under the constraint condition to obtain the next iteration point；
(3e) If at the point of iterationSatisfying the termination rule ∈>Is regarded as the optimal result, < >>The optimal cost is considered as the objective function and the calculation process is terminated; otherwise, the next step is carried out;
(3f) Setting k=k+1, returning to (3 b), and repeating the above steps.
2. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the fuel cell vehicle energy management method of claim 1.
3. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the fuel cell vehicle energy management method of claim 1.
4. An information data processing terminal for implementing the fuel cell vehicle energy management method of claim 1.
5. A fuel cell vehicle energy management system based on the fuel cell vehicle energy management method of claim 1, the fuel cell vehicle energy management system comprising:
the health model building module is used for representing the aging degree of the fuel cell voltage decline degree and the power cell capacity decline degree by the voltage decline degree and the power cell capacity decline degree of the fuel cell voltage decline degree and the power cell capacity decline degree, and building a full life cycle operation and maintenance cost model by combining the hydrogen consumption cost;
the model prediction control module is used for taking the full life cycle operation and maintenance cost as an objective function of model prediction control and comprises hydrogen consumption cost, fuel cell aging cost and power cell aging cost; determining constraint conditions according to normal operation ranges of all parts of the power system; selecting fuel cell output power variationAs control variables of the system, the state of charge SOC of the battery and the fuel cell output power +.>Is a state variable;
the control sequence pushing module is used for solving the established constraint-containing optimization problem by using sequence quadratic programming in each prediction time domain, deducing an optimal control sequence, applying a first value of the calculated optimal control sequence to a model, pushing the prediction view field forward further, repeatedly establishing an objective function and constraint conditions of the model prediction control problem, realizing rolling optimization of model prediction control, and completing real-time application of the energy management method in a driving period.
6. A fuel cell vehicle power system comprising the fuel cell vehicle energy management system of claim 5; the back end of the fuel cell is connected with the DC/DC converter and connected with the bus loop, and the lithium cell is directly connected with the bus.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311307416.3A CN117034666B (en) | 2023-10-10 | 2023-10-10 | Fuel cell automobile energy management method, system, medium, equipment and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311307416.3A CN117034666B (en) | 2023-10-10 | 2023-10-10 | Fuel cell automobile energy management method, system, medium, equipment and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117034666A CN117034666A (en) | 2023-11-10 |
| CN117034666B true CN117034666B (en) | 2023-12-19 |
Family
ID=88641683
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311307416.3A Active CN117034666B (en) | 2023-10-10 | 2023-10-10 | Fuel cell automobile energy management method, system, medium, equipment and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117034666B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117799503B (en) * | 2024-03-01 | 2024-04-30 | 西北工业大学 | Fuel cell heavy-duty truck energy management method and system considering energy-saving driving |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8332342B1 (en) * | 2009-11-19 | 2012-12-11 | The United States of America as represented by the Administrator of the National Aeronautics & Space Administration (NASA) | Model-based prognostics for batteries which estimates useful life and uses a probability density function |
| WO2021120798A1 (en) * | 2019-12-16 | 2021-06-24 | 金龙联合汽车工业（苏州）有限公司 | Energy control method for hybrid bus using hydrogen fuel battery and traction battery |
| CN113255205A (en) * | 2021-04-02 | 2021-08-13 | 南京工程学院 | Life cycle cost and battery temperature optimization method based on electric vehicle battery |
| WO2023284453A1 (en) * | 2021-07-15 | 2023-01-19 | 崔跃芹 | Cumulative consumption-based rechargeable battery life prediction method and apparatus, electronic device, and readable storage medium |
| CN115782700A (en) * | 2022-12-07 | 2023-03-14 | 北京理工大学 | Fuel cell hybrid vehicle energy management method considering battery thermal health constraint |
| CN116278993A (en) * | 2023-04-21 | 2023-06-23 | 吉林大学 | Multi-objective optimization considered fuel cell automobile energy management control method |
| CN116461391A (en) * | 2023-06-06 | 2023-07-21 | 东南大学 | Energy management method for fuel cell hybrid electric vehicle |
-
2023
- 2023-10-10 CN CN202311307416.3A patent/CN117034666B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8332342B1 (en) * | 2009-11-19 | 2012-12-11 | The United States of America as represented by the Administrator of the National Aeronautics & Space Administration (NASA) | Model-based prognostics for batteries which estimates useful life and uses a probability density function |
| WO2021120798A1 (en) * | 2019-12-16 | 2021-06-24 | 金龙联合汽车工业（苏州）有限公司 | Energy control method for hybrid bus using hydrogen fuel battery and traction battery |
| CN113255205A (en) * | 2021-04-02 | 2021-08-13 | 南京工程学院 | Life cycle cost and battery temperature optimization method based on electric vehicle battery |
| WO2023284453A1 (en) * | 2021-07-15 | 2023-01-19 | 崔跃芹 | Cumulative consumption-based rechargeable battery life prediction method and apparatus, electronic device, and readable storage medium |
| CN115782700A (en) * | 2022-12-07 | 2023-03-14 | 北京理工大学 | Fuel cell hybrid vehicle energy management method considering battery thermal health constraint |
| CN116278993A (en) * | 2023-04-21 | 2023-06-23 | 吉林大学 | Multi-objective optimization considered fuel cell automobile energy management control method |
| CN116461391A (en) * | 2023-06-06 | 2023-07-21 | 东南大学 | Energy management method for fuel cell hybrid electric vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 氢燃料电池汽车能量管理方法研究;孔岩;;汽车文摘(第05期);全文 * |
| 燃料电池电动汽车动力系统能量管理策略研究进展;武小花;邹佩佩;傅家豪;邓鹏毅;杨燕红;蔡云;曾昭炜;;西华大学学报(自然科学版)(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117034666A (en) | 2023-11-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Song et al. | Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability | |
| Hu et al. | Multi-objective energy management optimization and parameter sizing for proton exchange membrane hybrid fuel cell vehicles | |
| Chen et al. | An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle | |
| Xu et al. | Application of Pontryagin's Minimal Principle to the energy management strategy of plugin fuel cell electric vehicles | |
| CN104627167B (en) | Hybrid vehicle energy managing method and system considering service life of battery | |
| CN112810504B (en) | Fuel cell automobile energy management method based on nonlinear prediction model control | |
| CN117034666B (en) | Fuel cell automobile energy management method, system, medium, equipment and terminal | |
| Zhang et al. | Varying-domain optimal management strategy for parallel hybrid electric vehicles | |
| Herrera et al. | Adaptive and non-adaptive strategies for optimal energy management and sizing of a dual storage system in a hybrid electric bus | |
| CN112319462B (en) | Energy management method for plug-in hybrid electric vehicle | |
| CN113682203B (en) | Energy regulation and control method based on full life cycle state of fuel cell tramcar | |
| Sun et al. | Real-time optimal EMS of adaptive charge sustenance for fuel cell hybrid buses based on driving characteristics recognition | |
| CN114940103A (en) | Fuel cell heavy truck power management method considering driving cost and service life cost | |
| Li et al. | Energy sources durability energy management for fuel cell hybrid electric bus based on deep reinforcement learning considering future terrain information | |
| Yu et al. | Integrated energy and thermal management strategy for extended range electric vehicle based on battery temperature and state-of-charge global planning | |
| Gong et al. | Energy Management for a DM-i Plug-in Hybrid Electric Vehicle via Continuous-Discrete Reinforcement Learning | |
| CN113352946A (en) | Energy management method of fuel cell automobile power system | |
| CN112072713A (en) | Power distribution network scheduling method and storage medium | |
| Misra et al. | Intelligent vehicle to grid based plug-in electric vehicle with cloud computing | |
| Guo et al. | Model continuity approximations and real-time nonlinear optimization in cost-optimal predictive energy management of fuel cell hybrid electric vehicles | |
| Tang et al. | Degradation adaptive energy management strategy for FCHEV based on the Rule-DDPG method: tailored to the current SOH of the powertrain | |
| CN117799503B (en) | Fuel cell heavy-duty truck energy management method and system considering energy-saving driving | |
| Ren et al. | Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle | |
| Sun et al. | Optimization of Energy Saving and Fuel-Cell Durability for Range-Extended Electric Vehicle | |
| CN113191520A (en) | Road network optimization method considering electric vehicle charging load |
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 |