A whole vehicle energy management method of a range extended electric vehicle

Abstract

The application relates to a whole vehicle energy management method of a range extended electric vehicle and relates to the technical field of hybrid vehicle energy management.The target trip of the vehicle is divided into separate operation segments according to the operation data, environment information and driving style of the driver of the vehicle, and the whole vehicle demand power and driving time of each operation segment are estimated.Taking the minimum fuel consumption in the trip as the target, the power generation power reference of the range extender in each operation segment in the driving process of the vehicle is planned by using an optimization algorithm, the power generation power of the range extender is corrected in real time based on the instantaneous demand power of the whole vehicle, and finally the steady state and transient state following between the power generation power of the range extender and the demand power of the whole vehicle in the trip is realized.The application reduces the charging and discharging times of the power battery on the basis of low fuel consumption, prolongs the service life of the power battery, and improves the operation economy of the whole life cycle of the range extended electric vehicle.

Description

Technical Field

[0001] This invention relates to the field of hybrid vehicle energy management, and more particularly to a method for managing the energy of a range-extended electric vehicle. Background Technology

[0002] Range-extended electric vehicles (REEVs) feature a completely decoupled range extender from wheel-end torque, allowing them to operate continuously within their high-efficiency power generation range. Furthermore, REEVs eliminate the need for multi-speed hybrid transmissions, simplifying the system structure, facilitating layout, and reducing vehicle weight and cost. REEVs possess significant market potential in the new energy vehicle sector.

[0003] Because the range extender does not directly drive the vehicle, and considering cost factors, the rated power of the range extender in range-extended electric vehicles (REEVs) is generally relatively small, and its efficient power generation range differs somewhat from the vehicle's required power range. During operation, a mismatch between the range extender's power generation and the vehicle's power demand is unavoidable, necessitating the charging and discharging of the battery to "smooth out peak loads." Current technologies, to achieve power matching between the range extender and the vehicle's demand, utilize high-precision maps and navigation information to predict road conditions ahead, allowing for the planning of the range extender's power generation during driving. However, existing strategies do not consider the transient response of the range extender's power generation to the vehicle's power demand; the range extender's power generation remains relatively constant. Furthermore, they do not consider energy losses during battery charging and discharging, nor the impact of charging and discharging on battery life and performance. Additionally, this strategy cannot automatically identify frequent load variations. Therefore, this strategy currently cannot achieve optimal vehicle operating economy. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, the present invention provides a vehicle energy management method for range-extended electric vehicles.

[0005] This invention provides a method for overall vehicle energy management of a range-extended electric vehicle, comprising: acquiring real-time vehicle operating data, environmental information, and historical operating data; estimating the overall vehicle mass and analyzing the driver's driving style using environmental information, real-time operating data, and historical vehicle operating data; dividing the target trip into multiple independent operating segments, and estimating the actual power demand of the vehicle for each independent operating segment by combining environmental information, overall vehicle mass, and driver's driving style; solving for the range extender's power demand benchmark under various operating conditions using a multi-objective optimization algorithm; and correcting the range extender's output power benchmark Px1 based on the fluctuation of the vehicle's real-time power demand to obtain the current real-time output power demand value of the range extender.

[0006] Furthermore, real-time operating data includes: accelerator pedal opening, brake pedal opening, steering wheel angle, gear information, range extender output power, drive motor input and output power, accessory input power, and power battery SOC; environmental information includes: weather, temperature, humidity, wind speed, road congestion, traffic flow, road gradient, road conditions, and road surface material.

[0007] Furthermore, the overall vehicle weight is estimated using real-time operational data and environmental information, including:

[0008] Rolling resistance F f The expression is: F f =mg·f,

[0009] Air resistance F w The expression is:

[0010] Slope resistance F i The expression is: F i =mg·sinα,

[0011] Acceleration resistance F j The expression is: In the formula: the formula for calculating δ is:

[0012] When a vehicle is moving, the total resistance ΣF experienced by the vehicle is the rolling resistance F. f air resistance F w Acceleration resistance F j Slope resistance F i The sum: ΣF=F f +F w +F i +F j Vehicle driving force F t The relationship between the vehicle's driving force and the motor's output torque is equal to the total resistance force ΣF acting on the vehicle. The total vehicle weight can be obtained by combining the above formulas.

[0013] The letters in the above formulas have the following meanings: m represents the total mass of the vehicle (kg), g represents the acceleration due to gravity (m / s²). 2 ), C D The drag coefficient is represented by A, and the frontal area (m²) is represented by A. 2 ), u r The relative speed is expressed as (km / h), α represents the road gradient, δ represents the rotational mass conversion factor, and du / dt represents the vehicle acceleration (m / s²). 2 ), I W The moment of inertia I of the wheel W =+I W2 IW1 For the rotational inertia of the driving wheel and I W2 For non-driving wheel rotation I W1 Inertia (kg·m) 2 ), I f The moment of inertia of a flywheel (kg·m) 2 ), i0 represents the main drive ratio, i g η represents the gear ratio of the transmission. T This indicates the mechanical efficiency of the transmission system.

[0014] Furthermore, the driver's driving style is evaluated using parameters from five aspects of historical vehicle operation data: vehicle speed, acceleration, pedal operation, time ratio, and steering wheel control.

[0015] Furthermore, the step of dividing the target trip into multiple independent running segments includes: dividing the target trip into multiple segments according to road surface type and road gradient, and then further dividing each segment into separate running segments according to road congestion and traffic flow conditions in the environmental information.

[0016] Furthermore, in each independent operating segment, the average power demand of the drive motor and the vehicle travel time are estimated by using environmental information such as road slope, road type, vehicle weight, and vehicle speed parameters predicted by the driver's driving style. The power demand of the drive motor in a single operating segment is added to the average power of the accessories to obtain the total power demand of the vehicle in that single operating segment. The accessories include the vehicle's oil pump, air pump, DC-DC power supply, and air conditioning.

[0017] Furthermore, the step of solving for the range extender power requirement benchmark under various operating conditions using a multi-objective optimization algorithm includes:

[0018] Establish the objective function to minimize energy consumption: Ti represents the operating time of each operating segment, Pxi represents the range extender output power reference of each operating segment, and ηxi represents the range extender power generation efficiency of each operating segment.

[0019] Determine the constraints, which include:

[0020] First constraint: The current SOC of the power battery is BatSOC, the target SOC lower limit is BatSOC_low, the upper limit is BatSOC_high, the total battery capacity of the power battery is BatCap, and δWk is the change in the power battery capacity of the whole vehicle.

[0021] Second constraint: -BatCap×Wave_low≤δWk≤BatCap×Wave_high; The lower limit of the power battery SOC fluctuation is Wave_low, and the upper limit is Wave_high;

[0022] The third constraint is: FC_Pw_idel≤Pxk≤FC_Pw_rated; the third constraint means that the range extender output power reference is between the idle power FC_Pw_idel and the rated power FC_Pw_rated.

[0023] Fourth constraint: If Pk>P_Veh_mean, then: Pk>Pxk>P_Veh_mean; if Pk≤P_Veh_mean, then: Pk≤Pxk≤P_Veh_mean; P_Veh_mean is the average energy consumption required by the vehicle during the target journey, and Pk is the actual power required by the vehicle.

[0024] Fifth constraint: If Pa>Pb>P_Veh_mean×1.2, then: Pxa>Pxb; Pa and Pb are the actual power requirements of the whole vehicle for any two separate operating segments, and Pxa and Pxb are the range extender output power references corresponding to Pa and Pb;

[0025] Genetic algorithm is used to iteratively optimize and obtain the range extender output power references Px1, Px2…Pxk…Pxn for each operating condition.

[0026] Furthermore, based on the difference between the range extender output power reference Pxk for a single segment and the vehicle's required power Pk for a single operating segment, the actual usable electricity of the vehicle in the range extender's output power during a single operating segment is calculated:

[0027] If Pk > Pxk: Wxk = Pxk × Tk; if Pk ≤ Pxk: Wxk = (Pk + (Pxk - Pk) × ηbat) × Tk, where ηbat is the charging and discharging efficiency of the power battery. The change in the power battery charge δWk during vehicle operation is:

[0028]

[0029] Furthermore, the process of the genetic algorithm includes:

[0030] The chromosomes that design the output power reference of the range extender: The genes on the chromosomes of individuals in the population represent the output power references Px1, Px2, ..., Pxk, ..., Pxn of the range extender under each operating condition. The n operating conditions are represented by indices 1, 2, ..., k, ..., n. The numerical range represented by each gene is [FC_Pw_idel, FC_Pw_rated].

[0031] Initialize the population: Randomly generate n sequences of natural numbers in the range [FC_Pw_idel,FC_Pw_rated] to represent the output power reference of the range extender under each operating condition, forming a chromosome. If the chromosome does not meet all the constraints, regenerate the chromosome.

[0032] Construct the fitness function:

[0033] Among them, it is checked whether the chromosome meets all the constraints. If it does, the range extender power generation efficiency is determined by looking up the table or the fitted curve. The objective function fuel consumption Q is determined by the range extender output power and power generation efficiency of the individual running segment. If it does not meet the constraints, the fitness is assigned a minimum value.

[0034] The fitness of all chromosomes in the population is sorted from high to low, and the top half of the chromosomes are selected as the parents and passed on to the offspring, thus generating a new population.

[0035] The above genetic algorithm is used to iteratively optimize and obtain the range extender output power references Px1, Px2...Pxk...Pxn for each operating condition.

[0036] Furthermore, when generating offspring, the generation of random numbers is compared with the crossover rate to determine whether crossover is necessary. If so, the offspring chromosomes are generated with the left and right sides derived from the parent chromosomes. A crossover point is randomly set on the chromosomes to divide them into left and right parts. When generating offspring, the generation of random numbers is compared with the mutation rate to determine whether mutation is necessary. If so, the gene to be mutated is assigned a value within the range [FC_Pw_idel, FC_Pw_rated] and a new random natural number is generated.

[0037] The technical solutions provided in the embodiments of the present invention have the following advantages compared with the prior art:

[0038] This invention provides a vehicle energy management method for range-extended electric vehicles. It estimates vehicle mass and driver's driving style using operational data and environmental information. The target trip is divided into individual operating segments, each considered a steady-state condition. Combining environmental information, vehicle mass, and driver's driving style, the actual power demand of the vehicle is estimated for each independent operating segment. Using minimum fuel consumption as the optimization objective and battery SOC fluctuation as a constraint, a genetic algorithm based on objective optimization calculates the range extender output power benchmark for each individual operating segment, achieving steady-state power output of the range extender following the vehicle's power demand. The range extender's power output benchmark is corrected based on real-time changes in the vehicle's power demand, achieving transient response of the range extender's power output to changes in the vehicle's power demand. This invention achieves minimum fuel consumption for the vehicle's target trip under constraints, significantly reducing overall vehicle operating costs. It also minimizes battery SOC fluctuations during the trip, reducing the number of charge-discharge cycles and extending battery life. This improves the overall power generation efficiency of range-extended electric vehicles, enables efficient and stable operation of the range extender, reduces the number of charge and discharge cycles of the power battery, extends the lifespan of the range extender and power battery, and maximizes the operational economy of the entire vehicle throughout its life cycle. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating a method for managing the energy of a range-extended electric vehicle provided by the present invention;

[0042] Figure 2 The flowchart provided by the present invention is for solving the range extender power requirement benchmark under various operating conditions using a multi-objective optimization algorithm.

[0043] Figure 3 A flowchart of the genetic algorithm for implementing multi-objective optimization provided by this invention;

[0044] Figure 4 This is a schematic diagram of a vehicle energy management system for a range-extended electric vehicle provided by the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] See Figure 1 As shown, this embodiment of the invention provides a method for managing the energy of a range-extended electric vehicle, including:

[0048] S1. Acquire real-time vehicle operating data, environmental information, and historical operating data. For example... Figure 1 As shown, after the vehicle is powered on, the vehicle's onboard terminal obtains real-time operating data through the OBD interface. Simultaneously, the onboard terminal also obtains environmental information and historical vehicle operating data stored on a cloud server via a high-speed network. Real-time operating data includes accelerator pedal opening, brake pedal opening, steering wheel angle, gear information, range extender output power, drive motor input and output power, accessory input power, and battery SOC. Accelerator pedal opening, brake pedal opening, steering wheel angle, and gear information are control parameters, while range extender output power and drive motor input and output power are power parameters. Environmental information includes: weather, temperature, humidity, wind speed, road congestion, traffic flow, road gradient, road conditions, and road surface material. Weather, temperature, humidity, and wind speed are climatic conditions; road congestion and traffic flow are traffic conditions; road gradient is terrain conditions; and road conditions and road surface material are road surface quality.

[0049] S2. Estimate the overall vehicle mass and analyze the driver's driving style by using environmental information, real-time operating data, and historical vehicle operating data.

[0050] In one embodiment of the present invention, the vehicle weight is estimated using real-time operating data and environmental information, specifically including:

[0051] When a vehicle is moving, the total resistance ΣF experienced by the vehicle is the rolling resistance F.f air resistance F w Acceleration resistance F j Slope resistance F i The sum: ΣF=F f +F w +F i +F j Among them, rolling resistance F f The expression is: F f = mg·f, air resistance F w The expression is: Slope resistance F i The expression is: F i =mg·sinα, acceleration resistance F j The expression is: In the formula: the formula for calculating δ is: The relationship between vehicle driving force and motor output torque is as follows:

[0052] The letters in the above formulas have the following meanings: m represents the total mass of the vehicle (kg), g represents the acceleration due to gravity (m / s²). 2 ), C D The drag coefficient is represented by A, and the frontal area (m²) is represented by A. 2 ), u r The relative speed is expressed as (km / h), α represents the road gradient, δ represents the rotational mass conversion factor, and du / dt represents the vehicle acceleration (m / s²). 2 ), I W The moment of inertia I of the wheel W =I W1 +I W2 I W1 For the rotational inertia of the driving wheel and I W2 The moment of inertia of the non-driving wheels (kg·m) 2 ), I f The moment of inertia of a flywheel (kg·m) 2 ), i0 represents the main drive ratio, i g η represents the gear ratio of the transmission. T This indicates the mechanical efficiency of the transmission system.

[0053] When a vehicle is moving, the driving force F of the vehicle t It equals the total resistance force ΣF acting on the vehicle. Solving the above formulas simultaneously yields the total mass m of the vehicle.

[0054] In one embodiment of the present invention, five parameters of the vehicle's historical operating data—vehicle speed, acceleration, pedal operation, time ratio, and steering wheel control—are used as characteristic parameters of the driver's driving style, and the driver's driving style is evaluated using these characteristic parameters.

[0055] S3. Divide the target journey into multiple independent running segments. In the specific implementation process, when the target journey is known, the road surface type and road gradient in the environmental information are also known. Divide the target journey into multiple segments according to the road surface type and road gradient. Each segment is then further divided into separate running segments according to the road congestion and traffic flow conditions in the environmental information.

[0056] When the driver sets a destination, the target trip is the distance between the current vehicle location and the destination; when the driver does not set a destination, the target trip is the forward predicted trip returned by the driving assistance map.

[0057] S4. Estimate the actual power demand of the whole vehicle for each independent operating segment.

[0058] In each independent operating segment, the average power demand of the drive motor and the vehicle travel time are estimated by combining the vehicle speed parameters predicted from road slope, road type, and driver driving style with the vehicle mass calculated in S2. The actual power demand of the vehicle for that individual operating segment is obtained by adding the drive motor power demand of the individual operating segment to the average power demand of the accessories. These accessories include the vehicle's oil pump, air pump, DC-DC power supply, and air conditioning.

[0059] If the cloud server has a relatively complete energy consumption data map, the actual power demand of the whole vehicle and the vehicle driving time can also be obtained by looking up the energy consumption data map through a high-speed network.

[0060] S5. Solve the range extender power requirement baseline under various operating conditions using a multi-objective optimization algorithm.

[0061] See Figure 3 As shown, the range extender power requirement baseline under various operating conditions is solved using a multi-objective optimization algorithm, including:

[0062] S5-1. Treat each independent running segment as a steady-state condition, and obtain the relevant parameters for each condition as shown in the table below:

[0063]

[0064]

[0065] S5-2. Establish the objective function to minimize energy consumption.

[0066] Let the total energy consumption, i.e., fuel consumption, be Q. Then the objective function for minimizing energy consumption is:

[0067] Ti represents the operating time of each operating segment, Pxi represents the range extender output power reference of each operating segment, and ηxi represents the range extender power generation efficiency of each operating segment.

[0068] S5-3. Determine the constraints:

[0069] Based on the difference between the range extender output power reference Pxk for a single segment and the vehicle's required power Pk for a single operating segment, the actual usable electricity of the vehicle in the range extender's output power during a single operating segment is calculated.

[0070] If Pk > Pxk: Wxk = Pxk × Tk; if Pk ≤ Pxk: Wxk = (Pk + (Pxk - Pk) × ηbat) × Tk, where ηbat is the charging and discharging efficiency of the power battery. The change in the power battery charge δWk during vehicle operation is:

[0071]

[0072] At the end of the trip, the SOC of the power battery should be within the target SOC range. Let the current SOC of the power battery be BatSOC, the lower limit of the target SOC be BatSOC_low, the upper limit be BatSOC_high, and the total capacity of the power battery be BatCap. Therefore, the first constraint condition can be obtained:

[0073]

[0074] Throughout the vehicle's operation, the charge and discharge capacity of the power battery must be kept within a certain range. This limitation can simultaneously reduce the number of charge and discharge cycles of the power battery. Let the lower limit of the power battery's SOC be Wave_low and the upper limit be Wave_high, then the second constraint condition can be obtained: -BatCap×Wave_low≤δWk≤BatCap×Wave_high.

[0075] The range extender's output power reference is between the idle power FC_Pw_idel and the rated power FC_Pw_rated, so the third constraint condition can be obtained: FC_Pw_idel≤Pxk≤FC_Pw_rated.

[0076] Preferably, let the average energy consumption of the entire vehicle during the target journey be P_Veh_mean. To improve computational efficiency, a fourth constraint is added:

[0077] If Pk > P_Veh_mean, then: Pk > Pxk > P_Veh_mean;

[0078] If Pk≤P_Veh_mean, then: Pk≤Pxk≤P_Veh_mean;

[0079] Preferably, let Pa and Pb be the actual power requirements of the vehicle for any two individual operating segments, respectively. To further improve computational efficiency, a fifth constraint is added:

[0080] If Pa > Pb > P_Veh_mean × 1.2, then: Pxa > Pxb.

[0081] S5-4. Optimization is performed using a genetic algorithm. (See also...) Figure 3 As shown, the optimization calculation process of the genetic algorithm includes:

[0082] The chromosomes that design the output power reference of the range extender: The genes on the chromosomes of individuals in the population represent the output power references Px1, Px2, ..., Pxk, ..., Pxn of the range extender under each operating condition. The n operating conditions are represented by indices 1, 2, ..., k, ..., n. The numerical range represented by each gene is [FC_Pw_idel, FC_Pw_rated].

[0083] Initialize the population: Randomly generate n sequences of natural numbers in the range [FC_Pw_idel, FC_Pw_rated] to represent the output power reference of the range extender under each operating condition, forming a chromosome. If the chromosome does not meet all the constraints, regenerate the chromosome.

[0084] Fitness calculation: Construct a fitness function and determine the range extender's power generation efficiency by looking up a table or a fitted curve. In this embodiment, the fitted curve for the range extender's power generation efficiency (%) is as follows:

[0085] ηxk=51.42365+0.04778×Pxk-0.00102×Pxk 2 .

[0086] The objective function, fuel consumption Q, can be determined by analyzing the output power and power generation efficiency of the range extender in a single running segment. Lower fuel consumption results in higher fitness of the chromosome. The fitness function is expressed as follows:

[0087]

[0088] Check if the chromosome satisfies all constraints. If it does, calculate the fitness using the fitness function. If it does not, assign a minimum value to the fitness.

[0089] The fitness of all chromosomes in the population is sorted from high to low, and the top half of the chromosomes are selected as the parents and passed on to the offspring, thus creating a new population.

[0090] When generating offspring, the generated random number is compared with the crossover rate to determine whether crossover is necessary. If so, the offspring chromosomes are derived from the parent chromosomes on both sides. A crossover point is randomly set on the chromosome to divide it into left and right parts.

[0091] When generating offspring, the generated random number is compared with the mutation rate to determine whether mutation is needed. If so, the gene to be mutated is assigned a value within the range of [FC_Pw_idel, FC_Pw_rated] and a new random natural number is generated.

[0092] To obtain more accurate results while controlling runtime, the algorithm terminates its iteration when the set number of iterations is reached or the difference in fitness between two iterations is no greater than the accuracy; otherwise, it continues iterating.

[0093] Using the genetic algorithm described above, after the number of iterations terminates, the optimized range extender output power references Px1, Px2…Pxk…Pxn for each operating condition are output.

[0094] S6. The range extender's output power reference Pxk is corrected by the fluctuation of the vehicle's real-time power demand to obtain the current real-time output power demand value of the range extender.

[0095] During driving, steps S1-S6 are repeated to refresh the power generation benchmark of the range extender and upload the vehicle's operating data to the cloud server to improve the vehicle's energy consumption map.

[0096] Example 2

[0097] This embodiment provides a vehicle energy management system for a range-extended electric vehicle, implementing a method for managing the vehicle's energy. (See reference...) Figure 4 As shown, the vehicle-mounted terminal is the central hub for data collection, information acquisition, and core algorithm execution. The vehicle-mounted terminal acquires real-time vehicle operating data via the OBD interface. It also obtains real-time vehicle location information and environmental information about the vehicle's route via a high-speed satellite network. Environmental information includes climate conditions (weather, temperature, humidity, wind speed), traffic conditions (road congestion, traffic flow), terrain conditions (road slope), and road surface quality (road condition, road material). Using the acquired data and information, the vehicle-mounted terminal calculates the optimal output power of the range extender during vehicle operation through optimization algorithms. Simultaneously, all data can be stored on a cloud server via satellite, and combining operating data from vehicles of the same model can create a big data effect. A vehicle energy consumption map can be established using a large amount of real-time vehicle operating data, environmental information, driving style, and overall vehicle energy consumption data. The vehicle-mounted terminal can acquire the vehicle energy consumption map in real-time via a high-speed network built on satellite communication.

[0098] In the embodiments provided by this invention, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the circuit description and division are only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling shown or discussed may be indirect coupling through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0099] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for managing the energy of a range-extended electric vehicle, characterized in that, include: Acquire real-time vehicle operating data, environmental information, and historical operating data; estimate the vehicle mass and analyze the driver's driving style using environmental information, real-time operating data, and historical vehicle operating data; divide the target trip into multiple independent operating segments, and estimate the actual power demand of the vehicle for each independent operating segment by combining environmental information, vehicle mass, and driver's driving style; with minimum fuel consumption as the optimization objective, solve for the range extender's power demand benchmark under various operating conditions using a multi-objective optimization algorithm, including: Establish the objective function to minimize energy consumption: ; Ti This indicates the execution time of each runtime segment. Pxi This represents the range extender output power reference for each operating segment. ηxi This indicates the power generation efficiency of the range extender in each operating segment; Determine the constraints, which include: First constraint: The current state of power batteries SOC for BatSOC ,Target SOC The lower limit is BatSOC_low The upper limit is BatSOC_high The total capacity of the power battery is BatCap , δWk This refers to the change in the battery charge level of the entire vehicle. Second constraint: Power battery SOC The lower limit of the float is Wave_low The upper limit is Wave_high ; Third constraint: The third constraint indicates that the range extender's output power reference is at idle speed power. FC_Pw_idel With rated power FC_Pw_rated between; Fourth constraint: If Pk > P_Veh_mean ,but: Pk > Pxk > P_Veh_mean ;like Pk ≤ P_Veh_ mean ,but: Pk ≤ Pxk ≤ P_Veh_mean ; P_Veh_mean To set the average energy consumption of the entire vehicle during the target journey, Pk This represents the actual power required by the vehicle. Fifth constraint: If Pa > Pb > P_Veh_mean×1.2 ,but: Pxa > Pxb ; Pa , Pb These represent the actual power demand of the entire vehicle for any two individual operating segments. Pxa and Pxb are and Pa , Pb The corresponding range extender output power reference; The range extender output power benchmark for each operating condition is obtained through iterative optimization using a genetic algorithm. Px1, Px2…Pxk…Pxn The process includes: The chromosomes that define the output power benchmark for the range extender: Genes on the chromosomes of individual individuals in the population represent the output power benchmark of the range extender under various operating conditions. Px1, Px2…Pxk…Pxn , n Each working condition is indexed 1,2…k,…,n This indicates that the numerical range represented by each gene is [ FC_Pw_idel, FC_Pw_rated ]; Population initialization: randomly generated n indivual[ FC_Pw_idel, FC_Pw_rated The sequence of natural numbers within the range represents the output power reference of the range extender under each operating condition. If the chromosome does not meet all the constraints, the chromosome is regenerated. Construct the fitness function: ; Among them, it is checked whether the chromosome meets all the constraints. If it does, the range extender power generation efficiency is determined by looking up the table or the fitted curve. The objective function fuel consumption Q is determined by the range extender output power and power generation efficiency of the individual running segment. If it does not meet the constraints, the fitness is assigned a minimum value. The fitness of all chromosomes in the population is sorted from high to low, and the top half of the chromosomes are selected as the parents and passed on to the offspring, thus generating a new population. The above genetic algorithm is used to iteratively optimize and obtain the range extender output power benchmark for each operating condition. Px1, Px2…Pxk… Pxn ; The range extender's output power benchmark is corrected by the fluctuation of the vehicle's real-time power demand to obtain the current real-time output power demand value of the range extender.

2. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, Real-time operating data includes: accelerator pedal opening, brake pedal opening, steering wheel angle, gear information, range extender output power, drive motor input and output power, accessory input power, and power battery SOC; environmental information includes: weather, temperature, humidity, wind speed, road congestion, traffic flow, road gradient, road conditions, and road surface materials.

3. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, The overall vehicle weight is estimated using real-time operational data and environmental information, including: Rolling resistance The expression is: , air resistance The expression is: , Slope resistance The expression is: , Acceleration resistance The expression is: In the formula: the formula for calculating δ is: , The total resistance experienced by a vehicle when it is moving. Rolling resistance air resistance Acceleration resistance Slope resistance sum: Vehicle driving force Equal to the total resistance experienced by the vehicle The relationship between the vehicle's driving force and the motor's output torque is as follows: The total vehicle weight can be obtained by combining the above formulas. The letters in the above formulas have the following meanings: m represents the total mass of the vehicle (kg), g represents the acceleration due to gravity (m / s²). 2 ), The drag coefficient is represented by A, and the frontal area (m²) is represented by A. 2 ), u r The relative speed is represented by α (km / h), the road gradient by α, the rotational mass conversion factor by δ, and the vehicle acceleration by du / dt (m / s²). 2 ), The moment of inertia of a wheel =+ , For the rotational inertia of the drive wheel and Rotation of non-driving wheels Inertia (kg·m) 2 ), The moment of inertia of a flywheel (kg·m) 2 ), Indicates the main drive ratio. Indicates the gear ratio of the transmission. This indicates the mechanical efficiency of the transmission system.

4. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, The driver's driving style is evaluated using parameters from five aspects: vehicle speed, acceleration, pedal operation, time ratio, and steering wheel control, based on real-time and historical vehicle operation data.

5. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, The process of dividing the target trip into multiple independent running segments includes: dividing the target trip into multiple segments according to road surface type and road slope, and then further dividing each segment into separate running segments according to road congestion and traffic flow conditions in the environmental information.

6. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, In each independent operating segment, the average power demand of the drive motor and the vehicle travel time are estimated by using environmental information such as road slope, road type, vehicle weight, and vehicle speed parameters predicted by the driver's driving style. The total vehicle power requirement for a single operating segment is obtained by adding the drive motor power requirement of the individual operating segment to the average power of the accessories, which include the vehicle's oil pump, air pump, DC-to-DC power supply, and air conditioning.

7. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, Based on the range extender output power reference of individual segments Pxk The power demand of the whole vehicle in a single operating segment Pk The level of [value] is used to calculate the actual usable electricity of the entire vehicle from the electricity generated by the range extender in a single operating segment: like Pk > Pxk : Wxk = Pxk×Tk, like Pk ≤ Pxk : Wxk = ( Pk+ ( Pxk-Pk ) ×ηbat ) ×Tk ,in, η bat The charging and discharging efficiency of the power battery, and the change in the power battery capacity of the vehicle during driving. δWk for: 。 8. The vehicle energy management method for a range-extended electric vehicle according to claim 1, characterized in that, When generating offspring, the generated random number is compared with the crossover rate to determine whether crossover is needed. If needed, the offspring chromosomes are derived from the parent chromosomes on both sides. A crossover point is randomly assigned to the chromosome to divide it into left and right parts. When generating offspring, the generated random number is compared with the mutation rate to determine whether mutation is needed. If needed, the gene to be mutated is assigned a value of []. FC_Pw_idel, FC_Pw_rated Regenerate natural numbers randomly within the range of ]

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