CN110990996B - Electric vehicle operation system configuration method based on mathematical model - Google Patents
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Abstract
The invention belongs to the technical field of electric automobiles, and particularly relates to a method for configuring an electric automobile operation system based on a mathematical model, which mainly comprises a power system configuration model, a power battery model and a charging station distribution optimal model, the power system configuration model comprises a power consumption configuration model and an electric vehicle driving configuration model, the power battery configuration model comprises a battery SOC model, a battery charging model and a battery discharging model, through the analysis of mathematical modeling, the method provides important reference significance for optimizing the configuration of the battery, the power and the charging system, the central scheduling model is integrated with the configuration information of each model, mutual coordination and mutual cooperation of the models are realized through the central scheduling model, optimized operation of an electric automobile operation system is achieved, and reliable reference value can be provided for an electric automobile operation service unit.
Description
Technical Field
The invention belongs to the technical field of electric automobiles, and particularly relates to a configuration method of an electric automobile operation system based on a mathematical model.
Background
As the direction of automobile development, electric automobiles are acknowledged, and with the improvement of battery performance, the improvement of motor performance and optimization of a control system thereof and other electric automobile key technologies, electric automobiles have been vigorously developed as a representative of new energy automobiles. Electric vehicles are concerned and developed in various countries in the world, the overall performance of the electric vehicles is continuously improved, and along with the fact that electric vehicle systems are becoming more and more complex, operation modeling, particularly mathematical modeling, for the electric vehicles becomes an important process in the development process of the electric vehicle operation systems.
Model simulation for configuration of an electric vehicle running system is still in a preliminary exploration stage, and with development and growth of the electric vehicle market, how to reasonably and effectively establish an electric vehicle running mathematical model and realize optimized running of the electric vehicle by utilizing an optimization algorithm or an optimization model becomes an urgent need for optimizing the electric vehicle running system to the maximum extent.
Disclosure of Invention
The invention aims to provide a method for configuring an electric vehicle running system based on a mathematical model.
In order to solve the technical problem, the invention provides a method for configuring an electric vehicle operating system based on a mathematical model, which is characterized by comprising the following steps:
configuring a power system model, wherein the configuring the power system model comprises configuring a power consumption model and configuring an electric automobile driving model,
wherein the configuration consumption power model is configured according to equation (1):
formula (1):
wherein P isVThe power consumed by the electric automobile at the highest running speed, eta is transmission efficiency, g is gravity acceleration, f is rolling friction coefficient, m is full-load mass of the electric automobile, and v ismaxTo design the maximum vehicle speed, CdThe coefficient is aerodynamic resistance, and A is the windward area of the electric automobile;
the configuration electric automobile driving model is configured according to a formula (2):
formula (2):
wherein T iseFor the output of torque, i, of the motor shaft of the electric vehiclegIs the gear ratio of the transmission, ioIs the transmission ratio of the main reducer, eta is the transmission efficiency, r is the radius of the wheel, f is the rolling friction coefficient, maIs the mass of the vehicle body, g is the gravity plusSpeed, alpha being the gradient of the vehicle travelling on a slope, CdThe coefficient is aerodynamic resistance coefficient, A is windward area of the electric automobile, delta is automobile rotating mass conversion coefficient, and V is driving speed;
configuring a power battery model, wherein the configured power battery model comprises a battery SOC model, a battery charging model and a battery discharging model,
wherein the battery SOC model is configured according to equation (3):
formula (3)
Wherein the SOC0Is the initial charge of the battery, t0For the initial moment of discharge or charge, t1At the discharging or charging ending moment, i is charging or discharging current, i is negative during charging, i is positive during discharging, k is a charging and discharging coefficient, and Q is the rated capacity of the battery;
the battery charging model is configured according to formula (4), and the battery discharging model is configured according to formula (5):
formula (4)
Formula (5)
Wherein U ischargeFor charging open-circuit voltage, UdischargeTo the cell voltage at discharge, E0Is a voltage constant, k is a polarization constant, Q is the rated capacity of the battery, it is the charged electric quantity, A is the voltage amplitude of the polarization region, B is the reciprocal of the time constant of the polarization region, R is the internal resistance of the battery, i*Is the battery current through a first order low pass filter;
configuring a charging station distribution optimal model, wherein the charging station distribution optimal model is configured according to a formula (6),
equation (6) min F (x)1，x2，x3)＝max(Aix1+Bix2+Cix3+Di)-min(Aix1+Bix2+Cix3+Di) Wherein x is1、x2、x3The configuration proportions of the battery exchange station, the residential charging station and the public area charging station are respectively, Ai、Bi、Ci、DiThe configuration numbers of the battery replacement station, the residential charging station, the public area charging station and the rest charging stations are respectively;
the power system model, the power battery model and the optimal charging station distribution model are configured in the same system platform, and the three models in the system platform mutually exchange information.
Further, the configuration method further comprises the step of configuring a central scheduling model, wherein the central scheduling model comprises configuration information for acquiring the power system model, the power battery model and the optimal charging station distribution model, and the central scheduling model schedules the electric vehicles distributed in each area according to the configuration information.
Further, the configuration information includes power consumption, driving speed, battery level SOC, battery charging voltage, battery discharging voltage, and charging station distribution information.
Furthermore, the central dispatching model can provide peak shaving service in due time according to the configuration information, including control according to the charging station distribution information and the battery electric quantity SOC to provide timely charging service and road blockage control service according to the running speed of the electric vehicle.
Compared with the prior art, the invention has at least the following beneficial effects or advantages: the method is characterized in that analysis modeling is respectively carried out on main key module systems in the electric automobile system, important reference significance is provided for optimizing configuration of a battery, a power system and a charging system through analysis of mathematical modeling, a central scheduling model integrating configuration information of all models is further provided, mutual coordination and mutual cooperation of all models are realized through the central scheduling model, optimized operation of the electric automobile operation system is achieved, and reliable reference value can be provided for an electric automobile operation service unit.
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The invention will be described in further detail below with reference to the accompanying drawings:
FIG. 1 is a schematic diagram of a configuration model of an electric vehicle operating system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for configuring an electric vehicle operation system based on a mathematical model, as shown in fig. 1, the method comprises the following steps:
configuring a power system model, wherein the configuring the power system model comprises configuring a power consumption model and configuring an electric automobile driving model,
wherein the configuration consumption power model is configured according to equation (1):
formula (1):
wherein P isVThe power consumed by the electric automobile at the highest running speed, eta is transmission efficiency, g is gravity acceleration, f is rolling friction coefficient, m is full-load mass of the electric automobile, and v ismaxTo design the maximum vehicle speed, CdThe coefficient is aerodynamic resistance, and A is the windward area of the electric automobile;
the configuration electric automobile driving model is configured according to a formula (2):
formula (2):
wherein T iseFor the output of torque, i, of the motor shaft of the electric vehiclegIs the gear ratio of the transmission, ioIs the transmission ratio of the main reducer, eta is the transmission efficiency, r is the wheel radius, and f is the rollCoefficient of dynamic friction, maIs the body mass, g is the acceleration of gravity, α is the gradient of the vehicle traveling on a ramp, CdThe coefficient is aerodynamic resistance coefficient, A is windward area of the electric automobile, delta is automobile rotating mass conversion coefficient, and V is driving speed;
configuring a power battery model, wherein the configured power battery model comprises a battery SOC model, a battery charging model and a battery discharging model,
wherein the battery SOC model is configured according to equation (3):
formula (3)
Wherein the SOC0Is the initial charge of the battery, t0For the initial moment of discharge or charge, t1At the discharging or charging ending moment, i is charging or discharging current, i is negative during charging, i is positive during discharging, k is a charging and discharging coefficient, and Q is the rated capacity of the battery;
the battery charging model is configured according to formula (4), and the battery discharging model is configured according to formula (5):
formula (4)
Formula (5)
Wherein U ischargeFor charging open-circuit voltage, UdischargeTo the cell voltage at discharge, E0Is a voltage constant, k is a polarization constant, Q is the rated capacity of the battery, it is the charged electric quantity, A is the voltage amplitude of the polarization region, B is the reciprocal of the time constant of the polarization region, R is the internal resistance of the battery, i*Is the battery current through a first order low pass filter;
configuring a charging station distribution optimal model, wherein the charging station distribution optimal model is configured according to a formula (6),
equation (6) min F (x)1，x2，x3)＝max(Aix1+Bix2+Cix3+Di)-min(Aix1+Bix2+Cix3+Di) Wherein x is1、x2、x3The configuration proportions of the battery exchange station, the residential charging station and the public area charging station are respectively, Ai、Bi、Ci、DiThe configuration numbers of the battery replacement station, the residential charging station, the public area charging station and the rest of the charging stations are respectively.
The power system model, the power battery model and the optimal charging station distribution model are configured in the same system platform, and the three models in the system platform mutually exchange information.
The configuration method further comprises the step of configuring a central scheduling model, wherein the central scheduling model comprises configuration information for acquiring the power system model, the power battery model and the optimal charging station distribution model, and the central scheduling model schedules the electric vehicles distributed in each area according to the configuration information.
The configuration information comprises consumed power, running speed, battery electric quantity SOC, battery charging voltage, battery discharging voltage and charging station distribution information.
The central dispatching model can provide peak shaving service in due time according to the configuration information, control and provide charging service in time according to the charging station distribution information and the battery electric quantity SOC, and provide road blockage control service according to the running speed of the electric vehicle. In addition, the health condition of the battery can be acquired according to the charging voltage and the discharging voltage, and further, a prompt for whether to maintain or replace the battery is provided for the running of the electric automobile.
Compared with the prior art, the invention has at least the following beneficial effects or advantages: the method is characterized in that analysis modeling is respectively carried out on main key module systems in the electric automobile system, important reference significance is provided for optimizing configuration of a battery, a power system and a charging system through analysis of mathematical modeling, a central scheduling model integrating configuration information of all models is further provided, mutual coordination and mutual cooperation of all models are realized through the central scheduling model, optimized operation of the electric automobile operation system is achieved, and reliable reference value can be provided for an electric automobile operation service unit.
The above examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention, which is intended to be covered by the claims and any design similar or equivalent to the scope of the invention.
Claims (1)
1. A method for configuring an electric vehicle operation system based on a mathematical model is characterized by comprising the following steps:
configuring a power system model, wherein the configuring the power system model comprises configuring a power consumption model and configuring an electric automobile driving model,
wherein the configuration consumption power model is configured according to equation (1):
formula (1):
wherein P isVThe power consumed by the electric automobile at the highest running speed, eta is transmission efficiency, g is gravity acceleration, f is rolling friction coefficient, m is full-load mass of the electric automobile, and v ismaxTo design the maximum vehicle speed, CdThe coefficient is aerodynamic resistance, and A is the windward area of the electric automobile;
the configuration electric automobile driving model is configured according to a formula (2):
formula (2):
wherein T iseFor the output of torque, i, of the motor shaft of the electric vehiclegIs the gear ratio of the transmission, ioIs the transmission ratio of the main reducer, eta is the transmission efficiency, r is the radius of the wheel, f is the rolling friction coefficient, maIs the body mass, g is the acceleration of gravity, α is the gradient of the vehicle traveling on a ramp, CdThe coefficient is aerodynamic resistance coefficient, A is windward area of the electric automobile, delta is automobile rotating mass conversion coefficient, and V is driving speed;
configuring a power battery model, wherein the configured power battery model comprises a battery SOC model, a battery charging model and a battery discharging model,
wherein the battery SOC model is configured according to equation (3):
formula (3)
Wherein the SOC0Is the initial charge of the battery, t0For the initial moment of discharge or charge, t1At the discharging or charging ending moment, i is charging or discharging current, i is negative during charging, i is positive during discharging, k is a charging and discharging coefficient, and Q is the rated capacity of the battery;
the battery charging model is configured according to formula (4), and the battery discharging model is configured according to formula (5):
formula (4)
Formula (5)
Wherein U ischargeFor charging open-circuit voltage, UdischargeTo the cell voltage at discharge, E0Is a voltage constant, k is a polarization constant, Q is the rated capacity of the battery, it is the charged electric quantity, A is the voltage amplitude of the polarization region, B is the reciprocal of the time constant of the polarization region, R is the internal resistance of the battery, i*Is the battery current through a first order low pass filter;
configuring a charging station distribution optimal model, wherein the charging station distribution optimal model is configured according to a formula (6),
equation (6) min F (x)1，x2，x3)＝max(Aix1+Bix2+Cix3+Di)-min(Aix1+Bix2+Cix3+Di) Wherein x is1、x2、x3Arrangements for battery exchange stations, residential charging stations, public area charging stations, respectivelyRatio, Ai、Bi、Ci、DiThe configuration numbers of the battery replacement station, the residential charging station, the public area charging station and the rest charging stations are respectively;
configuring a central scheduling model, wherein the central scheduling model comprises configuration information for acquiring the power system model, the power battery model and the optimal charging station distribution model;
the central scheduling model schedules the electric vehicles distributed in each area according to the configuration information, and the specific scheduling mode is as follows: the central dispatching model can provide peak shaving service in due time according to the configuration information, and comprises the steps of controlling and providing timely charging service according to the charging station distribution information and the battery electric quantity SOC, and providing road blockage control service according to the running speed of the electric vehicle;
the configuration information comprises consumed power, driving speed, battery electric quantity SOC, battery charging voltage, battery discharging voltage and charging station distribution information;
the power system model, the power battery model and the optimal charging station distribution model are configured in the same system platform, and the three models in the system platform mutually exchange information.
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| CN109033531A (en) * | 2018-06-28 | 2018-12-18 | 浙江大学 | A kind of electric powered motor matching optimization method based on multiple objective programming |
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| US20060039221A1 (en) * | 2004-08-18 | 2006-02-23 | Sony Corporation | Memory card, memory card control method and memory card access control method |
| CN109033531A (en) * | 2018-06-28 | 2018-12-18 | 浙江大学 | A kind of electric powered motor matching optimization method based on multiple objective programming |
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