CN117021980A - Whole vehicle energy management method of range-extended electric vehicle - Google Patents
Whole vehicle energy management method of range-extended electric vehicle Download PDFInfo
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- 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
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- B60L50/60—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries
- B60L50/61—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries by batteries charged by engine-driven generators, e.g. series hybrid electric vehicles
- B60L50/62—Electric propulsion with power supplied within the vehicle using propulsion power supplied by batteries or fuel cells using power supplied by batteries by batteries charged by engine-driven generators, e.g. series hybrid electric vehicles charged by low-power generators primarily intended to support the batteries, e.g. range extenders
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- B60W40/06—Road conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
- B60W40/13—Load or weight
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/30—Driving style
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- 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
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Abstract
The invention relates to a whole vehicle energy management method of an extended range electric vehicle, and relates to the technical field of hybrid electric vehicle energy management. The invention divides the target journey of the vehicle into individual operation fragments through the operation data, the environment information, the driving style of the driver and the like of the vehicle, and estimates the whole vehicle required power and the running time of each operation fragment. And planning the power generation power reference of the range extender of each running segment in the running process of the vehicle by using an optimization algorithm with the minimum fuel consumption in the stroke as a target, and correcting the power generation power of the range extender in real time based on the instantaneous required power of the whole vehicle, so that the following of the steady state and the transient state between the power generation power of the range extender in the stroke and the required power of the whole vehicle is finally realized. The invention reduces the charge and discharge times of the power battery on the basis of lower fuel consumption, prolongs the service life of the power battery, and improves the running economy of the whole life cycle of the extended-range electric automobile.
Description
Technical Field
The invention relates to the field of hybrid electric vehicle energy management, in particular to a whole vehicle energy management method of an extended range electric vehicle.
Background
The range extender and the wheel end torque of the range-extending electric automobile are completely decoupled, and can continuously operate in a high-efficiency power generation interval. Meanwhile, the range-extended electric automobile does not need a multi-gear special gearbox, the system structure is simplified, and the weight and the cost of the automobile are reduced while the arrangement is convenient. The range-extended electric automobile has higher market potential in the field of new energy automobiles.
Because the range extender does not participate in the direct driving of the vehicle, and the rated power of the range extender electric pneumatic automobile is generally smaller in consideration of cost factors, the high-efficiency power generation interval of the range extender is a certain difference from the required power interval of the whole automobile. When the range-extended electric automobile runs, the situation that the generated power of the range extender is not matched with the required power of the whole automobile is avoided, and the power battery is required to be charged and discharged to cut off the peak and fill the valley. In the prior art, in order to realize the matching of the range extender and the required power of the whole vehicle, the operation condition of the whole vehicle can be predicted through the prediction of the high-precision map, navigation information and the like on the road information in front, so that the power generation power planning of the range extender in the driving process is realized. In the existing strategies, the transient following of the power generation power of the range extender to the power demand of the whole vehicle is not considered, and the power generation power of the range extender is relatively constant. The energy loss in the charging and discharging process of the power battery and the influence of the charging and discharging of the power battery on the service life and the performance of the power battery are not considered; meanwhile, the strategy cannot realize automatic identification of frequent load-changing working conditions. So the strategy can not realize the optimal running economy of the whole vehicle at present.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the invention provides a whole vehicle energy management method of an extended range electric vehicle.
The invention provides a whole vehicle energy management method of an extended range electric vehicle, which comprises the following steps: acquiring real-time running data, environment information and historical running data of a vehicle; estimating the quality of the whole vehicle and analyzing the driving style of a driver through environmental information, real-time operation data and vehicle history operation data; cutting a target journey into a plurality of independent operation fragments, and estimating the actual required power of the whole vehicle for each independent operation fragment by combining the environment information, the quality of the whole vehicle and the driving style of a driver; solving a range extender required power reference under each working condition by a multi-objective optimization algorithm; and correcting the output power reference Px1 of the range extender by the fluctuation of the real-time required power of the whole vehicle to obtain the real-time output power required value of the range extender at the current moment.
Still further, the real-time operational data includes: accelerator pedal opening, brake pedal opening, steering wheel angle, gear information, range extender output power, driving motor input and output power, accessory input power and power battery SOC; the environment information includes: weather, temperature, humidity, wind speed, road blockage, traffic flow, road grade, road condition, and pavement materials.
Further, the vehicle quality is estimated using real-time operational data and environmental information, including:
rolling resistance F f The expression of (2) is: f (F) f ＝mg·f，
Air resistance F w The expression of (2) is:
gradient resistance F i The expression of (2) is: f (F) i ＝mg·sinα，
Acceleration resistance F j The expression of (2) is:wherein: the calculation formula of delta is:
when the vehicle is running, the total resistance Σf received by the vehicle is the rolling resistance F f Air resistance F w Resistance to acceleration F j Slope resistance F i And (2) sum: Σf=f f +F w +F i +F j Vehicle driving force F t Equal to the total resistance Σf experienced by the vehicle, the relation between the vehicle driving force and the output torque of the motor is:the quality of the whole vehicle can be obtained by combining the formulas;
the meanings of the letters in the above formulae are as follows: m represents the mass (kg) of the whole vehicle, g represents the gravitational acceleration (m/s 2 )，C D Represents the air resistance coefficient, A represents the windward area (m 2 )，u r Represents relative speed (km/h), alpha represents road gradient, delta represents rotational mass conversion coefficient, du/dt represents vehicle acceleration (m/s) 2 )，I W Representing moment of inertia I of a wheel W ＝+I W2 ，I W1 For the moment of inertia and I of the drive wheel W2 For non-driving wheel rotation I W1 Inertia (kg.m) 2 )，I f Indicating the moment of inertia of the flywheel (kg.m) 2 )，i 0 Representing main gear ratio, i g Representing the speed ratio, eta of the transmission T Representing the mechanical efficiency of the transmission system.
Further, the driving style of the driver is evaluated by using the parameters of the vehicle speed, acceleration, pedal manipulation, time scale, steering wheel control of the vehicle history operation data as the characteristic parameters of the driving style of the driver.
Still further, the cutting the target trip into a plurality of independent run segments includes: dividing the target journey into a plurality of sectional journey according to the road surface type and road gradient, and dividing the single sectional journey into individual operation sections according to road congestion and traffic flow conditions in the environment information.
Further, in each independent running segment, estimating the average required power of the driving motor and the running time of the vehicle through the environmental information road gradient, road type, whole vehicle quality and the vehicle running speed parameters predicted by the driving style of the driver; and adding the driving motor required power of the independent operation segment and the average power of the accessory to obtain the whole vehicle required power of the independent operation segment, wherein the accessory comprises an oil pump, an air pump, a direct current-to-direct current power supply and an air conditioner of the vehicle.
Further, the solving the range extender required power reference under each working condition through the multi-objective optimization algorithm includes:
establishing a minimum energy consumption objective function:tn represents the running time of each running segment, pxn represents the range extender output power reference of each running segment, ηxn represents the range extender power generation efficiency of each running segment;
determining a constraint condition, the constraint condition comprising:
first constraint:the current SOC of the power battery is BatSOC, the lower limit of the target SOC is BatSOC_low, the upper limit of the target SOC is BatSOC_high, the total battery capacity of the power battery is BatCap, and delta Wk is the power battery electric quantity change of the whole vehicle;
second constraint: batCap x wave_low.ltoreq.δWk.ltoreq.BatCap x wave_high; the lower limit of the SOC floating of the power battery is wave_low, and the upper limit is wave_high;
third constraint: FC_Pw_idel is less than or equal to Pxk and less than or equal to FC_Pw_rate; the third constraint indicates that the range extender output power reference is between the idle power fc_pw_idel and the rated power fc_pw_rated;
fourth constraint: if Pk > p_veh_mean, then: pk > Pxk > P_Veh_mean; if Pk is less than or equal to P_Veh_mean, then: pk is less than or equal to Pxk and less than or equal to P_Veh_mean; P_Veh_mean is the average required energy consumption of the whole vehicle in the target travel, and Pk is the actual required power of the whole vehicle;
fifth constraint: if Pa > Pb > P_Veh_mean×1.2, then: pxa > Pxb; pa and Pb are respectively the actual required power of the whole vehicle of any two independent operation fragments, and Pxa and Pxb are output power references of the range extender corresponding to Pa and Pb;
and (5) adopting genetic algorithm iterative optimization to obtain the range extender output power references Px1 and Px2 … Pxk … Pxn of each working condition.
Further, according to the output power reference Pxk of the range extender of the single segment and the required power Pk of the whole vehicle of the single operation segment, the actual available electric quantity of the whole vehicle in the generated electric quantity of the range extender in the single operation segment is calculated:
if Pk > Pxk: wxk = Pxk ×tk, if Pk is not more than Pxk: wxk = (pk+ (Pxk-Pk) ×ηbat) ×tk, where ηbat is the charge-discharge efficiency of the power battery, and the power battery power change δwk of the whole vehicle during the running process of the whole vehicle is:
still further, the genetic algorithm comprises the steps of:
chromosome of output power reference of range extender is designed: genes on chromosomes of population individuals respectively represent output power references Px1 and Px2 … Pxk … Pxn of range extenders under various working conditions, n working conditions are represented by indexes 1,2 … k, … and n, and the numerical range represented by the genes is [ FC_Pw_idel and FC_Pw_rated ];
initializing a population: randomly generating natural number sequences in n [ FC_Pw_idel, FC_Pw_rate ] ranges to represent output power references of the range extender under each working condition to form a chromosome, and regenerating the chromosome if the chromosome does not meet all constraint conditions;
constructing a fitness function:
whether the chromosome meets all constraint conditions is checked, if yes, the power generation efficiency of the range extender is determined through table lookup or fitting curve checking, the fuel consumption Q of the objective function is determined through the output power and the power generation efficiency of the range extender of the independent operation segment, and if not, the fitness is given a minimum value;
sequencing the fitness of all chromosomes in the population from high to low, and selecting half of the chromosomes with the top rank as parent inheritance to offspring, thereby generating a new population;
and obtaining the range extender output power references Px1 and Px2 … Pxk … Pxn of each working condition through iterative optimization by the genetic algorithm.
Further, when generating offspring, judging whether the offspring chromosome needs to be crossed or not by comparing the generated random number with the crossing rate, if so, the left side and the right side of the generated offspring chromosome are respectively from the parent chromosome; randomly setting a crossing point of the chromosome to divide the chromosome into a left part and a right part; when generating offspring, judging whether to carry out mutation according to the comparison of the generated random number and the mutation rate, if so, carrying out assignment on the genes needing mutation within the range of [ FC_Pw_idel and FC_Pw_rate ] to generate natural numbers again randomly.
Compared with the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:
the whole vehicle energy management method of the extended range electric vehicle provided by the invention estimates the whole vehicle quality and the driving style of a driver through operation data and environmental information; the target travel condition is split into separate operating segments, each of which is considered a steady state condition. Carrying out actual demand power estimation on each independent operation segment by combining the environment information, the whole vehicle quality and the driving style of a driver; and calculating to obtain the output power reference of the range extender of each independent operation segment by taking the minimum fuel consumption as an optimization target and taking the fluctuation quantity of the SOC of the power battery as a constraint condition and a genetic algorithm based on target optimization, so that the power generation power steady state of the range extender is along with the required power of the whole vehicle. And the power generation power reference of the range extender is corrected through the real-time required power change of the whole vehicle, so that the transient response of the power generation power of the range extender to the required power change of the whole vehicle is realized. The invention realizes the minimum fuel consumption of the target travel of the vehicle under the constraint condition, and obviously reduces the running cost of the whole vehicle. The invention ensures that the SOC fluctuation of the power battery in the stroke is small, namely the charge and discharge times of the power battery are small, and the service life of the power battery is prolonged. Therefore, the power generation efficiency of the whole range extender of the electric automobile is improved, the efficient and stable operation of the range extender is realized, meanwhile, the charge and discharge times of a power battery are reduced, the service lives of the range extender and the power battery are prolonged, and the operation economy of the whole life cycle of the whole automobile is improved to the greatest extent.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a whole vehicle energy management method of an extended range electric vehicle provided by the invention;
FIG. 2 is a flow chart of solving a range extender required power reference under each working condition through a multi-objective optimization algorithm;
FIG. 3 is a flow chart of a genetic algorithm for achieving multi-objective optimization provided by the present invention;
fig. 4 is a schematic diagram of an entire vehicle energy management system of an extended range electric vehicle provided by the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Referring to fig. 1, an embodiment of the present invention provides a whole vehicle energy management method of an extended range electric vehicle, including:
s1, acquiring real-time running data, environment information and historical running data of a vehicle. As shown in fig. 1, after the whole vehicle is powered on, the vehicle-mounted terminal of the vehicle obtains real-time running data of the vehicle through the OBD interface. Meanwhile, the vehicle-mounted terminal also acquires environment information and vehicle historical operation data stored by the cloud server through the high-speed network. The real-time operation data comprise accelerator pedal opening, brake pedal opening, steering wheel angle, gear information, range extender output power, driving motor input and output power, accessory input power and power battery SOC. The accelerator pedal opening, the brake pedal opening, the steering wheel angle and the gear information belong to control parameters, and the output power of the range extender and the input and output power of the driving motor belong to power parameters. The environment information includes: weather, temperature, humidity, wind speed, road blockage, traffic flow, road grade, road condition, and pavement materials. Wherein, weather, temperature, humidity and wind speed belong to climate conditions; road jam condition and traffic flow belong to traffic condition; road grade belongs to a terrain condition; road conditions and pavement materials belong to the quality of pavement.
S2, estimating the quality of the whole vehicle and analyzing the driving style of a driver through environmental information, real-time operation data and vehicle historical operation data.
In one embodiment of the invention, the whole vehicle quality is estimated by using real-time operation data and environment information, and the method specifically comprises the following steps:
when the vehicle is running, the total resistance Σf received by the vehicle is the rolling resistance F f Air resistance F w Resistance to acceleration F j Slope resistance F i And (2) sum: Σf=f f +F w +F i +F j Wherein the rolling resistance F f The expression of (2) is: f (F) f =mg·f, air resistance F w The expression of (2) is:gradient resistance F i The expression of (2) is: f (F) i =mg·sin α, acceleration resistance F j The expression of (2) is:wherein: the calculation formula of delta is: />The relation between the driving force of the vehicle and the output torque of the motor is: />
The meanings of the letters in the above formulae are as follows: m represents the mass (kg) of the whole vehicle, g represents the gravitational acceleration (m/s 2 )，C D Represents the air resistance coefficient, A represents the windward area (m 2 )，u r Represents relative speed (km/h), alpha represents road gradient, delta represents rotational mass conversion coefficient, du/dt represents vehicle acceleration (m/s) 2 )，I W Representing moment of inertia I of a wheel W ＝I W1 +I W2 ，I W1 For the moment of inertia and I of the drive wheel W2 Is the non-driving wheel moment of inertia (kg.m) 2 )，I f Indicating the moment of inertia of the flywheel (kg.m) 2 )，i 0 Representing main gear ratio, i g Representing the speed ratio, eta of the transmission T Representing the mechanical efficiency of the transmission system.
When the vehicle is running, the vehicle driving force F t Equal to the total resistance experienced by the vehicleForce Σf. And the total mass m of the vehicle can be obtained by combining the formulas.
In one embodiment of the present invention, the driving style of the driver is evaluated by using the parameters of the vehicle speed, acceleration, pedal manipulation, time scale, and steering wheel control of the vehicle history operation data as the characteristic parameters of the driving style of the driver.
S3, cutting the target travel into a plurality of independent operation fragments. In the specific implementation process, under the condition that the target journey is known, the corresponding road surface type and road gradient in the environment information are also known. Dividing the target journey into a plurality of sectional journey according to the road surface type and road gradient, and dividing the single sectional journey into individual operation sections according to road congestion and traffic flow conditions in the environment information.
When a driver sets a destination, the target journey is the journey between the current vehicle position and the destination; when the driver does not set the destination, the target trip is a forward predicted trip returned from the driving assistance map.
S4, estimating the actual required power of the whole vehicle for each independent operation segment.
In each independent running segment, the average required power of the driving motor and the running time of the vehicle are estimated by combining the vehicle running speed parameters predicted by the road gradient, the road type and the driving style of the driver and the total vehicle quality calculated in the step S2. And adding the required power of the driving motor of the independent operation segment with the average power of the accessories to obtain the actual required power of the whole vehicle of the independent operation segment. Wherein the accessories comprise an oil pump, an air pump, a direct current-to-direct current power supply and an air conditioner of the vehicle.
If the cloud server has a perfect energy consumption data Map graph, the actual required power of the whole vehicle and the running time of the vehicle can be obtained by looking up the energy consumption data Map graph through a high-speed network.
S5, solving the range extender required power reference under each working condition through a multi-objective optimization algorithm.
Referring to fig. 3, solving the range extender required power reference under each working condition by using a multi-objective optimization algorithm includes:
s5-1, regarding each independent operation segment as a steady-state working condition, and acquiring relevant parameters under each working condition as shown in the following table:
s5-2, establishing a minimum energy consumption objective function.
Assuming that the total energy consumption, i.e. the fuel consumption, is Q, the minimization energy consumption objective function is:
s5-3, determining constraint conditions:
and calculating the actual available electric quantity of the whole vehicle in the electric quantity generated by the range extender in the single operation segment according to the output power standard Pxk of the range extender of the single operation segment and the required power Pk of the whole vehicle in the single operation segment.
If Pk > Pxk: wxk = Pxk ×tk, if Pk is not more than Pxk: wxk = (pk+ (Pxk-Pk) ×ηbat) ×tk, where ηbat is the charge-discharge efficiency of the power battery, and the power battery power change δwk of the whole vehicle during the running process of the whole vehicle is:
at the end of the stroke, the SOC of the power battery should be within the target SOC range. The current SOC of the power battery is set as BatSOC, the lower limit of the target SOC is BatSOC_low, the upper limit of the target SOC is BatSOC_high, and the total battery capacity of the power battery is BatCap, so that a first constraint condition can be obtained:
in the whole running process of the vehicle, the charge and discharge electric quantity of the power battery is kept within a certain range, and the limit can reduce the charge and discharge cycle times of the power battery at the same time. Let the lower limit of the SOC floating of the power battery be wave_low and the upper limit be wave_high, the second constraint condition can be obtained: batCap×wave_low.ltoreq.δWk.ltoreq.BatCap×wave_high.
The range extender output power reference is between the idle power fc_pw_idel and the rated power fc_ P w _rated, a third constraint is obtained: FC_Pw_idel is less than or equal to Pxk and FC_Pw_rate.
Preferably, the average required energy consumption of the whole vehicle in the target journey is set as p_veh_mean, and in order to improve the operation efficiency, a fourth constraint condition is added:
if Pk > p_veh_mean, then: pk > Pxk > P_Veh_mean;
if Pk is less than or equal to P_Veh_mean, then: pk is less than or equal to Pxk and less than or equal to P_Veh_mean;
preferably, the actual required power of the whole vehicle is Pa and Pb respectively by setting any two independent operation fragments, and in order to further improve the operation efficiency, a fifth constraint condition is added:
if Pa > Pb > P_Veh_mean×1.2, then: pxa > Pxb.
S5-4, optimizing by adopting a genetic algorithm. Referring to fig. 3, the genetic algorithm performs an optimization calculation process including:
chromosome of output power reference of range extender is designed: genes on chromosomes of individuals in the population represent output power references Px1 and Px2 … Pxk … Pxn of the range extender under various working conditions, n working conditions are represented by indexes 1,2 … k, … and n, and the numerical range represented by the genes is [ FC_Pw_idel and FC_Pw_rate ].
Initializing a population: and randomly generating natural number sequences in n [ FC_Pw_idel, FC_Pw_rate ] ranges, wherein the natural number sequences represent output power references of the range extender under various working conditions to form a chromosome, and regenerating the chromosome if the chromosome does not meet all constraint conditions.
Calculating the fitness: the fitness function is constructed, the power generation efficiency of the range extender is determined by looking up a table or looking up a fitting curve, and the fitting curve of the power generation efficiency (%) of the range extender in this embodiment is as follows:
ηxk＝51.42365+0.04778×Pxk-0.00102×Pxk 2 。
the fuel consumption Q of the objective function can be determined through the output power and the power generation efficiency of the range extender of the independent operation segment, the lower the fuel consumption is, the higher the fitness of the chromosome is, and the fitness function is expressed as follows:
and checking whether the chromosome meets all constraint conditions, if so, calculating the fitness through a fitness function, and if not, assigning a minimum value to the fitness.
The fitness of all chromosomes in the population is ordered from high to low, and half of the top chromosomes are selected as parents to inherit to offspring, so that a new population is generated.
When generating offspring, judging whether the offspring chromosome needs to be crossed or not by comparing the generated random number with the crossing rate, and if so, leading the left side and the right side of the generated offspring chromosome to come from the parent chromosome respectively. The chromosomes are randomly arranged at a crossing point to divide the chromosomes into left and right parts.
When generating offspring, judging whether to carry out mutation according to the comparison of the generated random number and the mutation rate, if so, carrying out assignment on the genes needing mutation within the range of [ FC_Pw_idel and FC_Pw_rate ] to generate natural numbers again randomly.
In order to obtain a more accurate result under the condition of controlling the running time, the iteration termination condition of the algorithm is to continue iteration when the set iteration times or the difference between the fitness of the two iterations is not greater than the accuracy.
Through the genetic algorithm, after the iteration times are ended, the range extender output power references Px1 and Px2 … Pxk … Pxn of all working conditions are output and optimized.
S6, correcting the output power reference Pxk of the range extender through fluctuation of the real-time required power of the whole vehicle to obtain the real-time output power required value of the range extender at the current moment.
And in the running process, repeating the steps S1-S6, refreshing the power generation power reference of the range extender, uploading the data in the running process of the vehicle to the cloud server, and perfecting the Map of the whole vehicle energy consumption.
Example 2
The embodiment provides a whole vehicle energy management system of an extended range electric vehicle, and a whole vehicle energy management method of the extended range electric vehicle is achieved. Referring to fig. 4, the vehicle-mounted terminal is a center for data acquisition, information acquisition and core algorithm operation. And the vehicle-mounted terminal acquires real-time running data of the vehicle through the OBD interface. The vehicle-mounted terminal also acquires real-time positioning information of the vehicle and environment information of a vehicle driving route in real time through a communication satellite high-speed network. The environmental information includes climate conditions (weather, temperature, humidity, wind speed), traffic conditions (road congestion, traffic flow), topography conditions (road gradient), road surface quality (road condition, road surface material). And the vehicle-mounted terminal calculates the optimal output power of the range extender in the running process of the vehicle through the optimization algorithm according to the acquired data and information. Meanwhile, all data can be stored to a cloud server through a communication satellite, and the running data of vehicles with the same model can be combined together to form a big data effect. And the whole vehicle energy consumption Map graph can be built through a large amount of real-time vehicle operation data, environment information, driving style and whole vehicle energy consumption data. The vehicle-mounted terminal can acquire the Map image of the whole vehicle energy consumption in real time through a high-speed network built by the communication satellite.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the described division of circuitry is merely a logical functional division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling shown or discussed as being coupled directly or indirectly to one another through some interface, device or unit, may be in the form of electrical, mechanical, or otherwise.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The whole vehicle energy management method of the extended range electric vehicle is characterized by comprising the following steps of: acquiring real-time running data, environment information and historical running data of a vehicle; estimating the quality of the whole vehicle and analyzing the driving style of a driver through environmental information, real-time operation data and vehicle history operation data; cutting a target journey into a plurality of independent operation fragments, and estimating the actual required power of the whole vehicle for each independent operation fragment by combining the environment information, the quality of the whole vehicle and the driving style of a driver; taking the minimum fuel consumption as an optimization target, and solving a range extender required power reference under each working condition through a multi-target optimization algorithm; and correcting the output power reference of the range extender by the fluctuation of the real-time required power of the whole vehicle to obtain the real-time output power required value of the range extender at the current moment.
2. The extended range electric vehicle energy management method of claim 1, wherein the real-time operation data comprises: accelerator pedal opening, brake pedal opening, steering wheel angle, gear information, range extender output power, driving motor input and output power, accessory input power and power battery SOC; the environment information includes: weather, temperature, humidity, wind speed, road blockage, traffic flow, road grade, road condition, and pavement materials.
3. The whole vehicle energy management method of extended range electric vehicle according to claim 1, wherein the whole vehicle quality is estimated using real-time operation data and environmental information, comprising:
rolling resistance F f The expression of (2) is: f (F) f ＝mg·f，
Air resistance F w The expression of (2) is:
gradient resistance F i The expression of (2) is: f (F) i ＝mg·sinα，
Acceleration resistance F j The expression of (2) is:wherein: the calculation formula of delta is: />
When the vehicle is running, the total resistance Σf received by the vehicle is the rolling resistance F f Air resistance F w Resistance to acceleration F j Slope resistance F i And (2) sum: Σf=f f +F w +F i +F j Vehicle driving force F t Equal to the total resistance Σf experienced by the vehicle, the relation between the vehicle driving force and the output torque of the motor is:the quality of the whole vehicle can be obtained by combining the formulas;
the meanings of the letters in the above formulae are as follows: m represents the mass (kg) of the whole vehicle, g represents the gravitational acceleration (m/s 2 )，C D Represents the air resistance coefficient, A represents the windward area (m 2 )，u r Represents relative speed (km/h), alpha represents road gradient, delta represents rotational mass conversion coefficient, du/dt represents vehicle acceleration (m/s) 2 )，I W Representing moment of inertia I of a wheel W ＝+I W2 ，I W1 For the moment of inertia and I of the drive wheel W2 For non-driving wheel rotation I W1 Inertia (kg.m) 2 )，I f Indicating the moment of inertia of the flywheel (kg.m) 2 )，i 0 Representing main gear ratio, i g Representing the speed ratio, eta of the transmission T Representing the mechanical efficiency of the transmission system.
4. The whole vehicle energy management method of extended range electric vehicle according to claim 1, wherein the driving style of the driver is evaluated by using the parameters of the vehicle in five aspects of speed, acceleration, pedal manipulation, time scale, steering wheel control of the vehicle in real time and history operation data as the characteristic parameters of the driving style of the driver.
5. The extended range electric vehicle energy management method of claim 1, wherein the cutting the target trip into a plurality of independent operating segments comprises: dividing the target journey into a plurality of sectional journey according to the road surface type and road gradient, and dividing the single sectional journey into individual operation sections according to road congestion and traffic flow conditions in the environment information.
6. The whole vehicle energy management method of extended range electric vehicle according to claim 1, wherein in each independent operation section, the average required power of the driving motor and the vehicle running time are estimated by the environmental information road surface gradient, road surface type, whole vehicle quality and vehicle running speed parameters predicted by the driving style of the driver; and adding the driving motor required power of the independent operation segment and the average power of the accessory to obtain the whole vehicle required power of the independent operation segment, wherein the accessory comprises an oil pump, an air pump, a direct current-to-direct current power supply and an air conditioner of the vehicle.
7. The whole vehicle energy management method of the extended range electric vehicle according to claim 1, wherein the solving the range extender required power reference under each working condition through the multi-objective optimization algorithm comprises:
establishing a minimum energy consumption objective function:tn represents the running time of each running segment, pxn represents the range extender output power reference of each running segment, ηxn represents the range extender power generation efficiency of each running segment;
determining a constraint condition, the constraint condition comprising:
first constraint:the current SOC of the power battery is BatSOC, the lower limit of the target SOC is BatSOC_low, the upper limit of the target SOC is BatSOC_high, the total battery capacity of the power battery is BatCap, and delta Wk is the power battery electric quantity change of the whole vehicle;
second constraint: batCap x wave_low.ltoreq.δWk.ltoreq.BatCap x wave_high; the lower limit of the SOC floating of the power battery is wave_low, and the upper limit is wave_high;
third constraint: FC_Pw_idel is less than or equal to Pxk and less than or equal to FC_Pw_rate; the third constraint indicates that the range extender output power reference is between the idle power fc_pw_idel and the rated power fc_pw_rated;
fourth constraint: if Pk > p_veh_mean, then: pk > Pxk > P_Veh_mean; if Pk is less than or equal to P_Veh_mean, then: pk is less than or equal to Pxk and less than or equal to P_Veh_mean; P_Veh_mean is the average required energy consumption of the whole vehicle in the target travel, and Pk is the actual required power of the whole vehicle;
fifth constraint: if Pa > Pb > P_Veh_mean×1.2, then: pxa > Pxb; pa and Pb are respectively the actual required power of the whole vehicle of any two independent operation fragments, and Pxa and Pxb are output power references of the range extender corresponding to Pa and Pb;
and (5) adopting genetic algorithm iterative optimization to obtain the range extender output power references Px1 and Px2 … Pxk … Pxn of each working condition.
8. The whole vehicle energy management method of the extended range electric vehicle according to claim 7, wherein the actual available electric quantity of the whole vehicle in the electric quantity generated by the range extender in the single operation segment is calculated according to the output power standard Pxk of the range extender of the single operation segment and the required power Pk of the whole vehicle in the single operation segment:
if Pk > Pxk: wxk = Pxk ×tk, if Pk is not more than Pxk: wxk = (pk+ (Pxk-Pk) ×ηbat) ×tk, where ηbat is the charge-discharge efficiency of the power battery, and the power battery power change δwk of the whole vehicle during the running process of the whole vehicle is:
9. the extended range electric vehicle energy management method of claim 7, wherein the genetic algorithm comprises:
chromosome of output power reference of range extender is designed: genes on chromosomes of population individuals respectively represent output power references Px1 and Px2 … Pxk … Pxn of range extenders under various working conditions, n working conditions are represented by indexes 1,2 … k, … and n, and the numerical range represented by the genes is [ FC_Pw_idel and FC_Pw_rated ];
initializing a population: randomly generating natural number sequences in n [ FC_Pw_idel, FC_Pw_rate ] ranges to represent output power references of the range extender under each working condition to form a chromosome, and regenerating the chromosome if the chromosome does not meet all constraint conditions;
constructing a fitness function:
whether the chromosome meets all constraint conditions is checked, if yes, the power generation efficiency of the range extender is determined through table lookup or fitting curve checking, the fuel consumption Q of the objective function is determined through the output power and the power generation efficiency of the range extender of the independent operation segment, and if not, the fitness is given a minimum value;
sequencing the fitness of all chromosomes in the population from high to low, and selecting half of the chromosomes with the top rank as parent inheritance to offspring, thereby generating a new population;
and obtaining the range extender output power references Px1 and Px2 … Pxk … Pxn of each working condition through iterative optimization by the genetic algorithm.
10. The whole vehicle energy management method of extended range electric vehicle according to claim 9, wherein when generating offspring, judging whether the offspring chromosome needs to be crossed by comparing the generated random number with the crossing rate, if so, the left and right sides of the offspring chromosome are respectively from parent chromosomes; randomly setting a crossing point of the chromosome to divide the chromosome into a left part and a right part; when generating offspring, judging whether to carry out mutation according to the comparison of the generated random number and the mutation rate, if so, carrying out assignment on the genes needing mutation within the range of [ FC_Pw_idel and FC_Pw_rate ] to generate natural numbers again randomly.
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