Method, system, storage medium and equipment for selecting power system of electric bus vehicle considering component parameter matching constraint

Abstract

The application belongs to the field of urban public transport operation management, and particularly relates to an electric bus power system selection method, system, storage medium and equipment. The application aims to solve the problem of unreasonable selection of electric bus power system, low energy efficiency and high cost caused by the fact that the existing research takes the power demand under extreme conditions as the selection benchmark and lacks systematic consideration of the parameter coupling relationship among the power battery, electric motor and reducer. The process is as follows: defining decision variables: defining decision variables related to the power battery; defining decision variables related to the electric motor; defining decision variables related to the reducer; establishing a power system selection model, the power system being the power battery, the electric motor and the reducer; and solving the power system selection model by using a particle swarm algorithm to obtain a power system selection scheme.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of urban public transport operation management, and particularly relates to an electric bus power system selection method, system, storage medium and equipment. BACKGROUND

[0002] As an important part of urban public transport, electric buses have been widely promoted and applied due to their zero emissions, low noise and high efficiency. The performance of the electric bus power system directly affects the energy consumption, range and reliability of the vehicle. Scientific and reasonable power system selection is a key link in the design and optimization of electric buses. Power system selection refers to determining the parameters of main components such as power batteries, electric motors, reducers and mechanical brakes for electric buses, such as battery capacity, electric motor peak torque, electric motor rated power, electric motor maximum speed, reducer reduction ratio, etc.

[0003] Existing power system selection methods mostly use the power demand of electric buses under extreme conditions as the selection benchmark, which is disconnected from the actual operation demand of urban bus routes. For some flat terrain bus routes or low-frequency short routes, it may lead to the selection of power batteries with excessive capacity and electric motors with excessive power, resulting in resource waste and increasing the purchase cost. In addition, existing research lacks systematic consideration of the parameter coupling relationship between power batteries, electric motors and reducers, which may result in mismatch between power batteries, electric motors and reducers, thereby restricting the power performance of the vehicle. Therefore, the present application proposes an electric bus power system selection method considering component parameter matching constraints. SUMMARY

[0004] The purpose of the present application is to solve the problem of unreasonable selection of electric bus power system, low energy efficiency and high cost due to the use of extreme conditions as the selection benchmark and the lack of systematic consideration of the parameter coupling relationship between power batteries, electric motors and reducers in existing research. Therefore, the present application proposes an electric bus power system selection method considering component parameter matching constraints.

[0005] The specific process of the electric bus power system selection method considering component parameter matching constraints is as follows:

[0006] Step 1, define decision variables:

[0007] Define decision variables related to power batteries;

[0008] Define decision variables related to electric motors;

[0009] Define decision variables related to reducers;

[0010] Step 2, establishing a power system selection model, the power system is a power battery, a motor and a reducer;

[0011] Step 3, solving the power system selection model by using a particle swarm algorithm to obtain a power system selection scheme. Preferably, the decision variables defined in step 1 are:

[0012] defining the decision variables related to the power battery;

[0013] defining the decision variables related to the motor;

[0014] defining the decision variables related to the reducer;

[0015] The specific process is:

[0016] Step 11, defining the decision variables related to the power battery; the specific process is: let the decision variable represent the rated capacity of the power battery of the electric bus; let the decision variable represent the open-circuit voltage of the power battery of the electric bus;

[0017] let the decision variable represent the number of series monomer batteries of each battery pack;

[0018] let the decision variable represent the number of parallel battery packs;

[0019] the power battery needs monomers;

[0020] and The relationship is shown in formula (1):

[0021]

[0022] In the formula, u cell represents the open-circuit voltage of the monomer battery;

[0023] and and The relationship is shown in formula (2):

[0024]

[0025] In the formula, Q cell represents the nominal capacity of the monomer battery;

[0026] Step 12, defining the decision variables related to the motor; the specific process is: let the decision variable represent the peak torque of the motor;

[0027] let the decision variable Pm represents the peak power of the electric motor;

[0028] Let decision variable Nmax represents the maximum rotating speed of the electric motor;

[0029] Let decision variable Tbr represents the rated braking torque of the electric motor;

[0030] Let decision variable Nt represents the turning rotating speed of the electric motor, as shown in equation (3):

[0031]

[0032] Step 13, defining decision variables related to the reducer; the specific process is as follows:

[0033] Let decision variable δ represent the reduction ratio of the reducer.

[0034] Preferably, the step 2 establishes a power system selection model; the specific process is as follows:

[0035] Step 21, constructing an optimization objective; the specific process is as follows:

[0036] The minimum operating cost of the electric bus fleet is taken as the optimization objective, as shown in equation (4):

[0037]

[0038] In the formula,

[0039] Z represents the operating cost of the electric bus fleet;

[0040] K represents the total number of electric buses in the electric bus fleet; k represents the kth electric bus;

[0041] Pb represents the purchase price of the unit capacity power battery;

[0042] Pm represents the purchase price of the unit peak torque electric motor;

[0043] Pm represents the price correction constant of the electric motor;

[0044] Pr represents the purchase price of the reducer;

[0045] Pb represents the purchase price of the electric bus body;

[0046] Pc represents the charging price;

[0047] T represents the service life of the electric bus power battery;

[0048] represents the daily power consumption of the electric bus k;

[0049] represents the retired health state of the power battery;

[0050] Δs k represents the daily average attenuation of the power battery of the electric bus k;

[0051] the first term of formula (4) is the purchase cost of the electric bus fleet, the second term of formula (4) is the charging cost of the electric bus fleet;

[0052] Step 22, setting the power battery life constraint; the specific process is:

[0053] ensuring that the service life of the power battery of the electric bus is greater than or equal to the expected service life:

[0054]

[0055] wherein, represents the expected service time of the power battery of the electric bus;

[0056] Step 23, constructing the service integrity constraint; the specific process is:

[0057] ensuring that at the end of the service life, the difference between the daily power consumption of the electric bus k and the daily charging capacity of the electric bus k is less than or equal to the available capacity of the power battery, as shown in formula (7):

[0058]

[0059] wherein,

[0060] represents the daily charging capacity of the electric bus k;

[0061] represents the upper limit of the state of charge of the power battery during daily operation;

[0062] represents the lower limit of the state of charge of the power battery during daily operation;

[0063] Step 24, constructing the power battery and electric motor matching constraint;

[0064] Step 25, constructing the electric motor and reducer matching constraint;

[0065] Step 26, constructing the electric bus fleet purchase budget constraint.

[0066] Preferably, the step 24 constructs a matching constraint between the power battery and the motor; the specific process is:

[0067] Step 241, ensuring the open-circuit voltage of the power battery of the electric bus is greater than or equal to the sum of the limit value of the voltage vector of the motor and the internal resistance voltage drop of itself, as shown in formulas (8) and (9):

[0068]

[0069] In the formula,

[0070] represents the internal resistance at the time of retirement;

[0071] PP represents the number of pole pairs;

[0072] M q represents the q-axis inductance;

[0073] R s represents the stator resistance;

[0074] ψ f represents the permanent magnet flux linkage;

[0075] represents the limit current of the inverter;

[0076] M d represents the d-axis inductance;

[0077] is the q-axis current limit value of the motor in the constant torque region, unit: A, and the calculation method is shown in formula (10):

[0078]

[0079] In the formula,

[0080] represents the Coulomb torque;

[0081] represents the mechanical friction loss coefficient;

[0082] Step 242, ensuring that the maximum output power of the power battery at the end of the service life is greater than or equal to the input power of the motor, as shown in formula (11):

[0083]

[0084] In the formula,

[0085] η inv represents the inverter efficiency;

[0086] represents the minimum value of the motor drive efficiency;

[0087] Step 243, ensuring the motor's breakaway speed greater than or equal to the minimum value of the motor's breakaway speed As shown in equation (12):

[0088]

[0089] In the formula,

[0090] represents the minimum value of the motor's breakaway speed.

[0091] Preferably, the step 25 builds the motor and reducer matching constraints; the specific process is as follows:

[0092] ensuring the motor's maximum speed satisfies equation (13) with the reducer's reduction ratio δ;

[0093] ensuring the motor's peak torque satisfies equation (14) with the reducer's reduction ratio δ;

[0094] ensuring the motor's rated braking torque satisfies equation (15) with the reducer's reduction ratio δ;

[0095]

[0096] In the formula,

[0097] L wheel represents the circumference of the motor bus wheel;

[0098] r represents the radius of the motor bus wheel;

[0099] v max represents the maximum speed of the motor bus driving on the line;

[0100] η drl represents the transmission system efficiency;

[0101] F max represents the maximum resistance that the motor bus needs to overcome when driving on the line;

[0102] represents the minimum value of the proportion of the motor's maximum braking force to the total required braking force;

[0103] F min represents the minimum resistance that the motor bus needs to overcome when driving on the line.

[0104] Preferably, the step 26 builds the motor bus fleet purchase budget constraints; the specific process is as follows:

[0105] Guarantee the purchase cost of the electric bus fleet is less than or equal to the purchase budget of the electric bus fleet As shown in formula (16):

[0106]

[0107] In the formula, represents the purchase budget of the electric bus fleet.

[0108] Preferably, the particle swarm algorithm is used to solve the power system selection model in step 3 to obtain a power system selection scheme; the specific process is as follows:

[0109] Step 31, set the number of particle swarms φ and the maximum number of iterations λ of the algorithm max , let the number of iterations λ of the algorithm be 1;

[0110] Step 32, consider the solution to the power system selection problem as the particle position X, that is,

[0111] Step 33, randomly generate the particle position X and judge whether the randomly generated particle position X satisfies the constraints (6)-(16);

[0112] If yes, add the randomly generated particle position X to the initial solution set;

[0113] If not, randomly generate the particle position X again until the number of particles in the initial solution set reaches φ;

[0114] Step 34, calculate the fitness function Z of the particle α using the objective function (4) α (λ), α = 1, 2,..., φ;

[0115] Step 35, sort Z α (λ) from large to small to determine the individual optimal value Pb α and the group optimal value Gb of the particle α;

[0116] Step 36, update the position and speed of the particle, as shown in formulas (17) and (18):

[0117]

[0118] X α (λ+1) = X α (λ) + V α (λ+1) (18)

[0119] In the formula,

[0120] V α (λ) and V α(λ+1) are the moving speeds of particle α at the λth and (λ+1)th iterations, respectively;

[0121] X α (λ) and X α (λ+1) are the positions of particle α at the λth and (λ+1)th iterations, respectively;

[0122] and are random numbers uniformly distributed in (0, 1];

[0123] μ is an inertia factor;

[0124] and are acceleration factors;

[0125] Step 37, judge whether the position X α (λ+1) of each particle satisfies the constraints (6)-(16) in turn;

[0126] If yes, the particle is reserved;

[0127] If no, return to step 36 until φ particles all satisfy the constraints (6)-(16), and enter step 38;

[0128] Step 38, judge whether λ> λ max ;

[0129] If no, let λ=λ+1, and return to step 34; if yes, calculate the fitness value of each particle, and output the particle position corresponding to the group optimal value Gb as the optimal solution of the power system selection problem, i.e. the final power system selection scheme.

[0130] The power system selection system for electric buses considering component parameter matching constraints is used to perform the power system selection method for electric buses considering component parameter matching constraints.

[0131] A storage medium has at least one instruction stored therein, and the at least one instruction is loaded and executed by a processor to implement the power system selection method for electric buses considering component parameter matching constraints.

[0132] The power system selection device for electric buses considering component parameter matching constraints comprises a processor and a memory; the memory has at least one instruction stored therein, and the at least one instruction is loaded and executed by the processor to implement the power system selection method for electric buses considering component parameter matching constraints.

[0133] The power system selection device for electric buses considering component parameter matching constraints comprises a processor and a memory; the memory has at least one instruction stored therein, and the at least one instruction is loaded and executed by the processor to implement the power system selection method for electric buses considering component parameter matching constraints.

[0134] ​The application takes minimizing the purchase cost and charging cost of the electric bus fleet as the target, takes the life of the power battery, service integrity, and the matching between the key components of the power system as the constraint condition, establishes a power system selection model for the electric bus vehicle, and solves the model by using a particle swarm algorithm. The method considers the individualized operation demand of different bus lines, realizes accurate selection, improves the matching degree of the electric bus power system and the actual bus line, quantizes the parameter matching relationship of the key components of the power system as the constraint condition, considers the power and economy demand, effectively avoids the unreasonable configuration phenomenon such as 'big horse pulling small cart' or'small horse pulling big cart' in the traditional selection method, and improves the overall energy efficiency of the electric bus system. BRIEF DESCRIPTION OF DRAWINGS

[0135] Figure 1 The flowchart of the application. DETAILED DESCRIPTION

[0136] Embodiment one: the specific process of the electric bus vehicle power system selection method considering component parameter matching constraints is as follows:

[0137] Step 1, define the decision variable:

[0138] define the decision variable related to the power battery;

[0139] define the decision variable related to the motor;

[0140] define the decision variable related to the reducer;

[0141] Step 2, establish the power system selection model, and the power system is the power battery, the motor and the reducer;

[0142] Step 3, solve the power system selection model by using the particle swarm algorithm, and obtain the power system selection scheme.

[0143] Embodiment two: the difference between this embodiment and embodiment one is that the decision variable is defined in step 1:

[0144] define the decision variable related to the power battery;

[0145] define the decision variable related to the motor;

[0146] define the decision variable related to the reducer;

[0147] The specific process is as follows:

[0148] Step 11, define the decision variable related to the power battery; the specific process is as follows:

[0149] Let decision variable denote the rated capacity of the power battery of the electric bus, in unit of kWh;

[0150] Let decision variable denote the open-circuit voltage of the power battery of the electric bus, in unit of V;

[0151] Let decision variable denote the number of series-connected single cells in each battery pack;

[0152] Let decision variable denote the number of parallel-connected battery packs;

[0153] The power battery needs single cells in total;

[0154] The relationship between is shown in equation (1):

[0155]

[0156] In the equation, u cell denotes the open-circuit voltage of the single cell, in unit of V;

[0157] The relationship between and is shown in equation (2):

[0158]

[0159] In the equation, Q cell denotes the nominal capacity of the single cell, in unit of kWh;

[0160] The nominal parameters of the single cell include the open-circuit voltage, the nominal capacity, and the internal resistance at the time of retirement;

[0161] Step 12, define decision variables related to the electric motor; the specific process is as follows:

[0162] Let decision variable denote the peak torque of the electric motor, in unit of N·m;

[0163] Let decision variable denote the peak power of the electric motor, in unit of W;

[0164] Let decision variable denote the maximum rotational speed of the electric motor, in unit of rad / min;

[0165] Let decision variable denote the rated braking torque of the electric motor, in unit of N·m;

[0166] Let decision variable denote the turning speed of the motor, in rad / min, is a function related to the peak torque and peak power of the motor, as shown in equation (3):

[0167]

[0168] Step 13, define the decision variable related to the reducer; the specific process is:

[0169] Let decision variable δ denote the reduction ratio of the reducer.

[0170] Other steps and parameters are the same as in the first embodiment.

[0171] The third embodiment is different from the first or second embodiment in that the power system selection model is established in step 2; the specific process is:

[0172] Step 21, construct the optimization objective; the specific process is:

[0173] The optimization objective is to minimize the operating cost of the electric bus fleet, as shown in equation (4):

[0174]

[0175] In the formula,

[0176] Z is the operating cost of the electric bus fleet, in yuan;

[0177] K represents the total number of electric buses in the electric bus fleet, in units of vehicles; k represents the kth electric bus.

[0178] denotes the purchase price of a unit capacity power battery, in yuan / kWh;

[0179] denotes the purchase price of a unit peak torque motor, in yuan / N·m;

[0180] denotes the price correction coefficient of the motor, in yuan;

[0181] denotes the purchase price of the reducer, in yuan;

[0182] denotes the purchase price of the electric bus body, in yuan;

[0183] denotes the charging price, in yuan / kWh;

[0184] is the service life of the power battery of the electric bus, in days;

[0185] represents the daily power consumption of the electric bus k, in kWh;

[0186] represents the retired health state of the power battery, in %;

[0187] Δs k represents the daily average attenuation of the power battery of the electric bus k, in kWh;

[0188] the first term of formula (4) is the purchase cost of the electric bus fleet, the second term of formula (4) is the charging cost of the electric bus fleet;

[0189] The total weight of the electric bus fleet is taken as the optimization objective, as shown in the following formula:

[0190]

[0191] In the formula,

[0192] m total is the total weight of the electric bus fleet, in kg;

[0193] K represents the total number of electric buses in the electric bus fleet, in units of vehicles; k represents the kth electric bus;

[0194] is the reciprocal of the energy density of the power battery, in kg / kWh, and is usually 6.0638 kg / kWh;

[0195] ξ′1 is the mass correction coefficient of the power battery, in kg, and is usually 25.577 kg;

[0196] ξ′2 is the conversion coefficient of the motor mass and peak torque, in kg / N·m, and is usually 0.174 kg / N·m;

[0197] ξ′3 is the motor mass correction coefficient, in kg, and is usually 32.9 kg;

[0198] ξ′4 is the mass of the reducer, in kg, and is usually 228 kg;

[0199] ξ′5 is the average passenger mass, in kg / person, and is usually 60 kg / person;

[0200] ξ′6 is the conversion coefficient of the number of seats and the vehicle body mass, in kg / seat, and is usually 226.87 kg / seat;

[0201] ξ′7 is a vehicle body mass correction coefficient, with the unit of kg, and usually takes the value of 1657.8 kg;

[0202] is a prescribed vehicle compartment crowdedness limit, with the unit of %;

[0203] is a rated passenger capacity of the electric bus, with the unit of person;

[0204] is a fixed passenger seat number of the electric bus compartment, with the unit of person;

[0205] The beneficial effects of the present application are:

[0206] The present application takes minimizing the total weight of the electric bus fleet as the target, takes the service integrity and the mutual matching among the key components of the power system as the constraint conditions, establishes a power system selection model of the electric bus, and solves the model by using a particle swarm algorithm. The method considers the individualized operation requirements of different bus lines, realizes accurate selection, and improves the matching degree of the electric bus power system and the actual bus line; the parameter matching relationship of the key components of the power system is quantified as a constraint condition, the requirements of power performance and lightweight are considered, the unreasonable configuration phenomena such as 'big horse pulling small cart' or'small horse pulling big cart' in the traditional selection method are effectively avoided, and the overall energy efficiency of the electric bus system is improved; the universality of the power system selection method of the electric bus is improved, which can provide technical reference for the vehicle selection decision of the bus enterprise and the product design of the manufacturing enterprise.

[0207] Two optimization targets of the present application are selected from 2;

[0208] Step 22, setting a power battery life constraint; the specific process is:

[0209] The service life of the power battery of the electric bus is greater than or equal to the expected service life:

[0210]

[0211] In the formula, represents the expected service time of the power battery of the electric bus, with the unit of day;

[0212] Step 23, constructing a service integrity constraint; the specific process is:

[0213] The difference between the daily power consumption of the electric bus k and the daily charging capacity of the electric bus k at the end of the service life (the last day of the service life) is less than or equal to the available capacity of the power battery, as shown in formula (7):

[0214]

[0215] wherein, represents daily charging capacity of the electric bus k, unit: kWh;

[0216] represents the upper limit of the state of charge of the power battery during daily operation, unit: %;

[0217] represents the lower limit of the state of charge of the power battery during daily operation, unit: %;

[0218] Step 24, constructing the power battery and motor matching constraint;

[0219] Step 25, constructing the motor and reducer matching constraint;

[0220] Step 26, constructing the electric bus fleet purchase budget constraint.

[0221] The other steps and parameters are the same as those in the first or second embodiment.

[0222] The fourth embodiment is different from the first to third embodiments in that the power battery and motor matching constraint in the step 24 is constructed, and the specific process is as follows:

[0223] Step 241, ensuring that the open circuit voltage of the power battery of the electric bus is greater than or equal to the sum of the internal resistance voltage drop ( or ) of the power battery and the limit value ( or ) of the motor voltage vector, as shown in formulas (8) and (9):

[0224]

[0225] wherein,

[0226] represents the internal resistance at the time of retirement, unit: Ω;

[0227] PP represents the number of pole pairs;

[0228] M q represents the q-axis inductance, unit: H;

[0229] R s represents the stator resistance, unit: Ω;

[0230] ψ f represents the permanent magnet flux linkage, unit: Wb;

[0231] represents the limit current of the inverter, unit: A;

[0232] M d Ld represents the d-axis inductance, unit: H;

[0233] Formula (8) is the power battery and motor matching constraint that the motor needs to meet in the constant torque area; formula (9) is the power battery and motor matching constraint that the motor needs to meet in the constant power area;

[0234] The nominal parameters of the motor include the number of pole pairs, the d-axis inductance, the q-axis inductance, the permanent magnet flux linkage, the stator resistance, the mechanical friction loss coefficient and the Coulomb torque;

[0235] Lq represents the q-axis current limit value of the motor in the constant torque area, unit: A, and the calculation method is shown in formula (10):

[0236]

[0237] In the formula,

[0238] Tc represents the Coulomb torque, unit: N·m;

[0239] K represents the mechanical friction loss coefficient;

[0240] Step 242, ensure that the maximum output power of the power battery at the end of the service life (the last day of the service life) is greater than or equal to the input power of the motor, as shown in formula (11):

[0241]

[0242] In the formula,

[0243] η inv η represents the inverter efficiency, unit: %;

[0244] η represents the minimum value of the motor drive efficiency, unit: %;

[0245] Step 243, ensure that the breakaway speed of the motor is greater than or equal to the minimum value of the breakaway speed of the motor as shown in formula (12):

[0246]

[0247] In the formula,

[0248] ω represents the minimum value of the breakaway speed of the motor, unit: rad / min.

[0249] The other steps and parameters are the same as one of the first to third embodiments.

[0250] Specific implementation five: this implementation is different from one to four: the step 25 of constructing motor and reducer matching constraints; the specific process is:

[0251] Ensure the maximum speed of the motor And the reducer speed ratio δ satisfy formula (13);

[0252] Ensure the peak torque of the motor And the relationship between the reducer speed ratio δ satisfy formula (14);

[0253] Ensure the rated braking torque of the motor And the relationship between the reducer speed ratio δ satisfy formula (15);

[0254]

[0255] In the formula,

[0256] L wheel Indicates the circumference of the electric bus wheel, unit: mm;

[0257] r indicates the radius of the electric bus wheel, unit: m;

[0258] v max Indicates the maximum speed of the electric bus driving on the line, unit: m / s;

[0259] η drl Indicates the efficiency of the transmission system, unit: %;

[0260] F max Indicates the maximum resistance that the electric bus needs to overcome when driving on the line, unit: N;

[0261] Indicates the minimum value of the proportion of the maximum braking force of the motor to the total required braking force, unit: %;

[0262] F min Indicates the minimum resistance that the electric bus needs to overcome when driving on the line, unit: N.

[0263] Other steps and parameters are the same as one to four.

[0264] Specific implementation six: this implementation is different from one to five: the step 26 of constructing electric bus fleet purchase budget constraints; the specific process is:

[0265] Ensure that the purchase cost of the electric bus fleet is less than or equal to the purchase budget of the electric bus fleet As shown in formula (16):

[0266]

[0267] In the formula, represents the purchase budget of the electric bus fleet, in yuan.

[0268] The other steps and parameters are the same as those in the first to fifth embodiments.

[0269] Embodiment Seven: The embodiment is different from one of the first to sixth embodiments in that the particle swarm algorithm is used to solve the power system selection model in the step 3 to obtain a power system selection scheme; the specific process is as follows:

[0270] Step 31, set the number of particles φ and the maximum number of iterations λ of the algorithm max , let the number of iterations λ of the algorithm be 1;

[0271] Step 32, regard the solution to the power system selection problem as the particle position X, that is,

[0272] Step 33, randomly generate the particle position X and judge whether the randomly generated particle position X satisfies the constraints (6) to (16);

[0273] If yes, add the randomly generated particle position X to the initial solution set;

[0274] If no, randomly generate the particle position X again until the number of particles in the initial solution set reaches φ;

[0275] Step 34, calculate the fitness function Z α (λ) of the particle α using the objective function (4), α = 1, 2,..., φ;

[0276] Step 35, sort Z α (λ) from large to small to determine the individual optimal value Pb α (φ = 100, for the individual optimal value Pb α of each particle when λ = 1, it is Z α (λ)), compare the Z α (λ) of the particles α in the λ+1th cycle and the λth cycle, and select the smaller value as the individual optimal value Pb α of the particle α in the λ+1th cycle, 100 individual optimal values Pb α are obtained in each cycle, and the global optimal value Gb(φ = 100, for the individual optimal value Pb α of the 100 particles when λ = 1, select 1 minimum value as the global optimal value Gb, compare the global optimal values Gb in the λ+1th cycle and the λth cycle, and select the smaller value as the global optimal value Gb in the λ+1th cycle) is determined;

[0277] Step 36, update the position (direction of movement) and velocity (speed of movement) of the particle, as shown in equations (17) and (18):

[0278]

[0279] X α (λ+1)=X α (λ)+V α (λ+1) (18)

[0280] In the formula,

[0281] V α (λ) and V α (λ+1) are the moving speed of particle α at the λth and (λ+1)th iteration, respectively;

[0282] X α (λ) and X α (λ+1) are the position of particle α at the λth and (λ+1)th iteration, respectively;

[0283] and are random numbers uniformly distributed between (0, 1];

[0284] μ is an inertia factor, usually taking a value of 0.6;

[0285] and are acceleration factors, usually taking a value of 0.2;

[0286] Step 37, determine whether the position X α (λ+1) of each particle satisfies the constraints (6)-(16) in turn;

[0287] If yes, the particle is retained;

[0288] If no, return to step 36 until φ particles all satisfy the constraints (6)-(16), and enter step 38;

[0289] Step 38, determine whether λ>λ max ;

[0290] If no, let λ=λ+1, and return to step 34; if yes, calculate the fitness value of each particle, and output the particle position corresponding to the group optimal value Gb as the optimal solution of the power system selection problem, i.e. the final power system selection scheme.

[0291] The other steps and parameters are the same as those in the first to sixth specific embodiments.

[0292] ​Eighth Embodiment: An electric bus vehicle power system selection system considering component parameter matching constraints, the system is used to execute the electric bus vehicle power system selection method considering component parameter matching constraints.

[0293] Ninth Embodiment: A storage medium, the storage medium stores at least one instruction, the at least one instruction is loaded and executed by the processor to realize the electric bus vehicle power system selection method considering component parameter matching constraints.

[0294] It should be understood that any method described in the present application can be provided as a computer program product, software or computerized method, which can include a non-transitory machine-readable medium having instructions stored thereon, which can be used to program a computer system, or other electronic device. The storage medium can include, but is not limited to, magnetic storage medium, optical storage medium; magneto-optical storage medium includes: read-only memory ROM, random access memory RAM, erasable programmable memory (such as EPROM and EEPROM) and flash layer; or other types of media suitable for storing electronic instructions.

[0295] Tenth Embodiment: The electric bus vehicle power system selection device considering component parameter matching constraints, the device includes a processor and a memory, it should be understood that any device described in the present application includes a processor and a memory, the device can also include other units, modules that display, interact, process, control, etc. through signals or instructions, and other functions;

[0296] The memory stores at least one instruction, the at least one instruction is loaded and executed by the processor to realize the electric bus vehicle power system selection method considering component parameter matching constraints.

[0297] The present application can also have other various embodiments, those skilled in the art can make various corresponding changes and modifications according to the present application without departing from the spirit and essence of the present application, but these corresponding changes and modifications should all belong to the protection scope of the claims attached to the present application.

Claims

1. A method for selecting a powertrain system of an electric bus vehicle considering component parameter matching constraints, characterized in that: The method specifically comprises the following steps: Step 1, defining decision variables: defining decision variables related to the power battery; defining decision variables related to the motor; defining decision variables related to the reducer; Step 2, establishing a power system selection model, the power system being the power battery, the motor and the reducer; Step 3, solving the power system selection model by using a particle swarm algorithm to obtain a power system selection scheme; The step 1 specifically comprises the following steps: Step 11, defining decision variables related to the power battery; the specific process is as follows: Let decision variable represents the rated capacity of the power battery of the electric bus Let decision variable represents the open circuit voltage of the traction battery of the electric bus Let decision variable represents the number of series-connected unit cells for each battery pack; Let decision variable represents the number of parallel battery packs; The power battery collectively needs a single cell; In relation to as shown in equation (1): (1) wherein Voc represents the open circuit voltage of the monobloc cell; With And The relationship as formula (2) is shown: (2) In the formula, denotes the nominal capacity of the individual cells; Step 12, defining decision variables related to the motor; The specific process is as follows: Let decision variable denotes the peak torque of the electric motor; Let decision variable represents the peak power of the electric motor; Let decision variable denotes the maximum rotational speed of the electric motor; Let decision variable denotes the motor rated braking torque; Let decision variable represents the motor breakaway speed, as shown in equation (3): (3) Step 13, defining decision variables related to the reducer; The specific process is as follows: Let decision variable denotes the reduction ratio of the reduction gear; The step 2 specifically comprises the following steps: Step 21, constructing an optimization objective; the specific process is as follows: Taking the minimum operating cost of the electric bus fleet as the optimization objective, as shown in formula (4): (4) (5) In the formula, Operating costs for a fleet of electric buses; K represents the total number of electric buses in the electric bus fleet; k represents the kth electric bus; represents the purchase price of the unit capacity power battery; represents the purchase price of a unit peak torque motor; represents a price correction constant for the electric motor; represents the purchase price of the retarder; represents the purchase price of the electric bus body; represents a charging price; The service life of the power battery for the electric bus; represents the daily power consumption of the electric bus k; represents the state of health of the power battery at retirement; represents the daily average attenuation of the power battery of the electric bus k; The first term of equation (4) is the acquisition cost of the electric bus fleet, the second term of equation (4) is the charging cost of the electric bus fleet; Step 22, setting a power battery life constraint; the specific process is as follows: Ensure that the service life of the power battery of the electric bus is greater than or equal to the expected service life: (6) In the formula, represents the expected use time length of the electric bus power battery; Step 23, constructing a service integrity constraint; the specific process is as follows: Ensure that the difference between the daily power consumption of the electric bus k and the daily charging capacity of the electric bus k at the end of the service life is less than or equal to the available capacity of the power battery, as shown in formula (7): (7) In the formula, represents the daily charging amount of the electric bus k; represents the upper limit of the state of charge of the power battery during daily operation; represents the lower limit of the state of charge of the power battery during daily operation; Step 24, constructing a power battery and motor matching constraint; Step 25, constructing a motor and reducer matching constraint; Step 26, constructing an electric bus fleet purchase budget constraint.

2. The method for selecting the power system of an electric bus vehicle considering the component parameter matching constraints according to claim 1, characterized in that: The step 24 specifically comprises the following steps: Step 241, ensuring the open circuit voltage of the power battery of the electric bus greater than or equal to the sum of the voltage drop of the internal resistance and the limit value of the motor voltage vector, as shown in formulas (8) and (9): (8) (9) In the formula, R represents the internal resistance at the time of retirement; PP represents the number of pole pairs; denotes the q-axis inductance; R represents the stator resistance; represents the permanent magnet flux linkage; denotes the limit current of the inverter; represents the d-axis inductance; The q-axis current limit value in the constant torque region of the motor, in A, is calculated by formula (10). (10) In the formula, denotes the Coulomb torque; represents the coefficient of mechanical friction losses; Step 242, ensure that the maximum output power of the power battery at the end of the service life is greater than or equal to the input power of the motor, as shown in formula (11): (11) In the formula, represents the inverter efficiency; denotes the minimum value of the motor drive efficiency; Step 243, ensuring the turning speed of the motor greater than or equal to the minimum value of the turning speed of the motor as shown in equation (12): (12) In the formula, represents the minimum value of the motor breakaway speed.

3. The method for selecting the power system of the electric bus vehicle considering the component parameter matching constraints according to claim 2, characterized in that: The step 25 specifically comprises the following steps: Guarantee the maximum speed of the motor With the reduction ratio of the reduction gear Satisfy equation (13); Guaranteeing peak torque of electric motor The relationship between the reduction ratio of the reduction gear and the reduction ratio of the motor satisfies formula (14). The relationship between the reduction ratio of the reduction gear and the reduction ratio of the motor satisfies formula (14). Ensure the rated braking torque of the motor With the reduction ratio of the reducer The relationship satisfies formula (15); (13) (14) (15) In the formula, This indicates the circumference of the wheels of the electric bus; r represents the radius of the electric bus wheel; Vmax represents the maximum speed at which the electric bus travels on the route; denotes the transmission system efficiency; Fmax represents the maximum resistance that needs to be overcome by the electric bus when driving on the route; represents the minimum value of the ratio of the maximum braking force of the electric motor to the total required braking force; represents the minimum resistance that the electric bus needs to overcome to travel on the route.

4. The method for selecting the power system of the electric bus vehicle considering the component parameter matching constraints according to claim 3, characterized in that: The step 26 specifically comprises the following steps: to ensure that the acquisition cost of the electric bus fleet is less than or equal to the acquisition budget of the electric bus fleet as shown in equation (16): (16) In the formula, represents the purchase budget of the electric bus fleet.

5. The method for selecting the power system of the electric bus vehicle considering the component parameter matching constraints according to claim 4, characterized in that: The step 3 specifically comprises the following steps: Step 31, set the number of particle groups , maximum iteration number of the algorithm , let the iteration number of the algorithm ; Step 32, viewing the solution to the power system sizing problem as a particle position i.e. ; Step 33, randomly generate particle position and judge whether the randomly generated particle position satisfies constraints (6)~(16); If satisfied, randomly generate a particle position Add initial solution set; If not, re-randomize particle positions until the number of particles in the initial solution reaches ; Step 34, calculate the fitness of the particle using the objective function (4) , ;​ Step 35, to Sort from large to small, determine the individual optimal value of particles And the group optimal value ; Step 36, updating the position and speed of the particle, as shown in formulas (17) and (18): (17) (18) In the formula, and are particles at the first and iteration; and are particles At the first and iteration; and are random numbers uniformly distributed between 0 and 1. is the inertial factor; and are both acceleration factors; Step 37, judging the position of each particle in turn whether constraints (6)-(16) are satisfied; If the condition is met, the particle is retained; If not, return to step 36 until all particles satisfy constraints (6)-(16), go to step 38; Step 38, judging whether ; If not, then the method returns to step 34. , and returns to step 34. If yes, the fitness value of each particle is calculated and the optimal value of the population is output The corresponding particle position As the optimal solution of the power system selection problem, i.e. the final power system selection scheme.

6. An electric bus vehicle power system selection system considering component parameter matching constraints, characterized in that the system is used to execute the electric bus vehicle power system selection method considering component parameter matching constraints according to any one of claims 1 to 5.

7. A storage medium, characterized by The storage medium stores at least one instruction, and the at least one instruction is loaded and executed by the processor to realize the electric bus vehicle power system selection method considering component parameter matching constraints according to any one of claims 1 to 5.

8. An electric bus powertrain sizing device considering component parameter matching constraints, characterized in that, The device comprises a processor and a memory, the memory storing at least one instruction loaded and executed by the processor to implement the method for selecting the power system of the electric bus vehicle considering the component parameter matching constraint according to any one of claims 1 to 5.

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