KR100273536B1 - Apparatus and method for predicting and extending distance covered by electric vehicle - Google Patents

Abstract

PURPOSE: An apparatus and method for predicting and extending a distance covered by an electric vehicle is provided to predict a distance covered by identifying driving habit of a driver, suggest a good driving habit to the driver, minimize energy consumption of sub devices. CONSTITUTION: A speed sensing part(10) senses a speed of an electric vehicle and outputs the same into an electric signal. An accelerator pedal sensing part(20) senses an operation of an accelerator pedal and outputs a pertinent signal. A brake pedal sensing part(30) senses an operation of a brake pedal and outputs a pertinent signal. A gearshift lever sensing part(40) senses an operation of a gearshift lever and outputs a pertinent signal. A control part(50) receives and stores signals from the sensing parts, predicts a distance covered by a driving speed and a battery energy, and outputs an optimal driving condition in a mode. A display part(60) displays the optimal driving condition output from the control part(50) to a driver. A storing part(51) stores an output signal of the speed sensing part(10). A counter(52) counts a time of operating the gearshift lever. An operating part(53) operates the distance covered by receiving data stored in the storing part. A comparing part(54) compares distances covered and battery energy consumed that are varied according to driver's driving habits.

Description

Prediction and extension device of electric vehicle and its method

1 is a block diagram of a mileage prediction and extension device for an electric vehicle used in the present invention.

Figure 2 is a flow chart of the operation distance prediction and extension device of the electric vehicle used in the present invention.

3 is a flowchart illustrating a driving habit mode subroutine operation of a driver in the mileage prediction and extension method of the electric vehicle according to the present invention.

4 is a flowchart illustrating a required traction calculation subroutine operation in the mileage prediction and extension method of the electric vehicle of the present invention.

5 is a flowchart illustrating a subroutine calculation of battery energy consumption history in the mileage prediction and extension method of a battery vehicle according to the present invention.

6 (a) is a vehicle speed graph at the time of measuring the mileage of the electric vehicle of the present invention.

Figure 6 (b) is a state diagram showing the required traction force in the hill running state of the electric vehicle of the present invention.

7 (a) is a basic circuit diagram according to the battery consumption of the electric vehicle of the present invention.

7 (b) is an equivalent circuit diagram according to the battery consumption of the electric vehicle of the present invention.

The present invention relates to a method for estimating and extending the mileage of an electric vehicle, and more particularly, to calculate a driving distance according to a driver's driving habit with limited battery energy, thereby improving the driver's driving habit, and improving energy of a sub-device. The present invention relates to an electric vehicle's mileage prediction and extension method that can extend the mileage by limiting consumption.

In the case of an electric vehicle, there is a problem in that the driving distance is shorter than the distance that can be substantially driven by the driving habit of the driver and the energy consumption of the sub-device while driving with limited battery energy.

Accordingly, an object of the present invention is to estimate the driving habits of a driver who drives by using limited battery energy in an electric vehicle, to predict a driving distance, and to suggest a driving habit to the driver by setting the best driving habit to improve the driving habit of the driver. In addition, the present invention is to provide a method for predicting and extending the mileage of an electric vehicle to extend the mileage by minimizing the energy consumption of the lower devices.

In order to achieve the above object, the present invention includes the steps of initializing all variables; Storing and driving driving habits of various drivers who drive by driving a vehicle; Calculating and calculating the required traction force of the vehicle by a set arithmetic formula; Calculating and calculating a battery energy consumption history according to a vehicle mileage by a set arithmetic formula and mode; Calculating an energy consumption table by calculating driving habits of the various modes of the drivers and the consumption history of the battery energy and the required traction force of the vehicle, and storing the energy consumption table in the storage unit; Calculating a current wheel power (Pwh) of the vehicle, calculating a current acceleration (a), calculating a mileage from the start to the current time, and predicting a distance that can be driven by the remaining battery energy; In the electric vehicle comprising the step of selecting the optimal driving habits of the various drivers stored in the storage unit before the start of the vehicle and during the driving, and suggesting to extend the mileage, the calculation of the required traction force of the electric vehicle when the vehicle tilt angle Calculating and calculating the received air resistance Fd; Calculating and calculating rolling resistance (Fr) received when the wheel of the vehicle is driven; Calculating and calculating the climbing force (Fg) received when the vehicle climbs the inclination angle; Calculating and calculating an acceleration Fa when the vehicle is driven; Calculating and calculating a required traction force (Ft) required for the car to climb the hill; And calculating and calculating wheel power (Pwh) of the wheel that drives the car to climb the hill.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating a mileage prediction and extension device for an electric vehicle according to the present invention, comprising: a vehicle speed detector 10 that detects a speed of an electric vehicle and outputs the electrical signal; An accelerator pedal detection unit 20 for sensing an operation state of the accelerator pedal and outputting a corresponding signal; A brake pedal detection unit 30 for detecting an operation state of the brake pedal and outputting a corresponding signal; A shift lever detecting unit 40 for detecting an operation state of a shift lever of a shift stage and outputting a corresponding signal; A control unit (50) for receiving and storing the signals of the sensing units and modeting them, calculating and predicting a driving distance based on battery energy and a traveling speed, and outputting an optimum driving state according to the mode; And a screen display unit 60 for displaying the optimal driving state output from the controller 50 to the driver.

The control unit 50 includes a storage unit 51 for storing the output signal of the vehicle speed detection unit 10; A counter 52 for counting a shift lever operating time of the shift detecting unit 40; A calculation unit 53 for calculating the travel distance by receiving the data stored in the storage unit 51; Comparing unit 54 for comparing the driving distance and the battery power consumption that varies depending on the driving habits of the various drivers to the calculation means 54.

2 is an operation sequence of the mileage prediction and extension device for an electric vehicle used in the present invention, the method comprising: storing and modeting driving habits of various drivers according to the driving of the vehicle (S200); Calculating and calculating the required traction force of the vehicle by a set arithmetic equation (S300); Calculating a battery energy consumption history according to the vehicle driving distance (S400); Forming and storing an energy consumption table by calculating driving habits of the various modes of the driver, a history of consumption of battery energy and a required traction force of the vehicle according to the driving habits (S500); Calculating a current wheel power of the vehicle, calculating a current acceleration, and calculating a driving distance from a starting point to a current time to predict a distance that can be driven by the remaining battery energy (S600); In step S700, before the start of the vehicle and during driving, the driving habit of the various driving drivers is selected to propose an optimal driving habit.

Referring to the operation of the present invention made as described above are as follows.

When power is applied to drive the electric vehicle, the controller 50 initializes all the variables used (S110).

When the electric vehicle achieves a driving state, the driving habit of the driver is moded (S200).

As shown in FIG. 3, the driver's driving habits are sensed and read by operating the shift lever of the shift stage (S210), by detecting and reading the operating state of the brake pedal (S220). The operation state is detected and read (S230).

The mode of the read data according to the driving habits of the driver is configured as shown in Table 1 below.

[Figure 1]

As shown in FIG. 6 (a), when the instantaneous speed of the current vehicle is obtained at the mileage measurement time,

α: accelerator pedal signal or brake pedal signal

T1: time at which the mileage is measured

Mileage S at current time T

S1: Mileage at time T1

As described above, the shift lever is operated, the brake is operated, and the driving habits of the driver whose driving distance is changed according to the operation state of the accelerator pedal are modulated (S240), and returned to the main program.

In driving a vehicle, the force conditions are the characteristics of the vehicle such as test weight, frontal area, drag coefficient rolling resistance coefficient, drive train efficiency, etc. It is obtained by the grade of road, etc., and the road load power is obtained by the required tracction force which can overcome drag force and rolling resistance.

Therefore, as shown in FIG. 6 (b), the required traction force Ft (S300) is obtained as shown in FIG. 4 when the automobile travels at the inclination angle [theta].

Calculate the air resistance (Fd) received when the car climbs the inclination angle (S310),

Calculates the rolling resistance (Fr) received when the wheel of the car is driven (S320),

Calculate the climbing force (Fg) received when the car climbs the inclination angle (S330),

By calculating an acceleration Fa when the vehicle is driven (S340),

If the car obtains the required traction force (Ft) required to climb the hill (S350),

Where Wt is the weight of the vehicle, g is the weight acceleration, θ is the angle of inclination,

A _f is the overall surface of the vehicle, p is the air density, C _d is the drag coefficient,

C _r is rolling resistance, I is moment of inertia, r is radius of TIRE,

K is the resistance correction factor.)

In addition, it calculates the wheel power (Pwh) of the wheel driving the car to climb the hill here (S360),

Pwh = V + Ft Return to the main program.

In addition, the wheel power is applied to the battery energy consumption history (S400) calculation formula shown in FIG.

The battery power is calculated as a force that maintains a certain speed, such as the slope acceleration state, while driving a vehicle.

Thus, the battery energy consumption history for the mileage S varies during the driving state of each vehicle.

Therefore, as shown in FIG. 7 (a), the basic expression represented by the voltage of the battery cell is

Where V _d is the voltage of the discharged cell, V ₀ is the voltage without load, I _d is the discharge current, and R _c is the effective resistance of the cell.

Therefore, in order to obtain a history of energy consumption of the battery, the discharge current I _d of the battery is applied and read (S410), and the voltage V _d of the discharged cell of the battery is applied and read (S420).

From experimental data, the test values of V ₀ and R _c are expressed as a function of the depth of discharge (DOD) of the battery.

Therefore, when calculating the voltage (V ₀ ) of the battery without a load (S430), the discharge amount of the battery

do.

Where t is time, T is discharge time to DOD, I ₃ is the reversal time corresponding to every 3 hours, C ₃ is the battery capacity corresponding to I ₃ , and g ₁ is the discharge rate effect parameter.

Therefore, if you recalculate the unloaded battery voltage (V ₀ ),

Becomes

Since R _c = F ₂ (DOD),

As shown in FIG. 7 (b), in the equivalent circuit of the battery consumption basic circuit shown in FIG. 7 (a), the effective resistance R _c of the cell is Becomes

(However, g ₁ = 0 for most battery types when inferred from battery test data).

Battery discharge do.

By rearranging the above-mentioned formulas to obtain the power Pb of the battery (S440),

Since (1) If you substitute,

Can be obtained.

Therefore, when the battery energy consumption history is calculated (S450),

Becomes

In addition, when calculating the battery energy consumption history consumed in the battery terminal (inverter) (S460),

Can be obtained.

(Pwh is the wheel power, ηd is the efficiency of the inverter, ηm is the efficiency of the motor, and ηp is the efficiency of the power train.)

The discharged voltage is changed to the latest one including the time increment according to equation (1) and the calculation is continued until the battery voltage falls below the cutout point or reaches the limit set value by the user. The program continues to run until the limit setting value is reached and returns to the main program.

The driving habit table of the various modes of drivers as described above, the required traction force (Ft) of the vehicle and the consumption history (Pb) of the battery energy are continuously stored in the storage unit 51 (S500). .

Subsequently, the wheel power Pwh of the vehicle driving to predict the driving distance is calculated according to the time change, and the energy consumption of the battery is calculated (S600).

In addition, by calculating the acceleration a of the running vehicle, the current acceleration of the vehicle is calculated according to the time change (S610).

(Where T2-T1 = 100μ sec.)

If the car calculates and calculates the mileage S from the departure point to the current driving time (S620),

(S1 is the mileage up to T1 time, S is the mileage up to the current time.)

Therefore, the wheel power Pwh value calculated above, the current acceleration a and the distance S to the present time are calculated by the set program, and the distance which can be driven by the energy of the limited battery currently remaining. It predicts and outputs to the output unit 60 (S630).

In addition, when the driving distance is to be extended by using the energy of the battery remaining in the driving vehicle, the mode is stored in the storage unit 51 and selects a mode in which the energy of the battery is the least consumed among the driving habit tables of various drivers. Suggested to the driver and by driving the energy of the lower device can extend the mileage (S700).

As described above, the present invention determines the driving habits of the driver who uses the limited battery energy in the electric vehicle to predict the driving distance and set the best driving habits to improve the driving habits of the driver so as to drive the driving habits to the driver. It proposes a method to predict and extend the mileage of an electric vehicle, which has the effect of extending the mileage by minimizing the energy consumption of the sub-devices.

Claims (2)

Initializing all variables; Storing and driving driving habits of various drivers who drive by driving a vehicle; Calculating and calculating the required traction force of the vehicle by a set arithmetic formula; Calculating and calculating a battery energy consumption history according to a vehicle mileage by a set arithmetic formula and mode; Calculating an energy consumption table by calculating driving habits of the various modes of drivers, a history of consumption of battery energy and a required traction force of a vehicle, and storing the energy consumption table in the storage unit; Calculating a current wheel power (Pwh) of the vehicle, calculating a current acceleration (a), calculating a mileage from the start to the current time, and predicting a distance that can be driven by the remaining battery energy; In the electric vehicle comprising the step of selecting the optimum driving habits of the various drivers stored in the storage unit before the start of the vehicle and during the driving, and proposing an extension of the mileage, the calculation of the required traction force of the electric vehicle is to increase the inclination angle Calculating and calculating an air resistance force Fd received at the time; Calculating and calculating rolling resistance (Fr) received when the wheel of the vehicle is driven; Calculating and calculating the climbing force (Fg) received when the vehicle climbs the inclination angle; Calculating and calculating an acceleration Fa when the vehicle is driven; Calculating and calculating a required traction force (Ft) required for the car to climb the hill; And calculating and calculating wheel power (Pwh) of the wheels driving the vehicle to climb the hill. The method of claim 1, wherein the calculating of the battery energy consumption history comprises: inputting a discharge current I _d of the battery to obtain an energy consumption history of the battery; Inputting the voltage V _d of the discharged cell of the battery; Calculating and calculating a voltage V _o of the battery without a load; Calculating and calculating the power Pb of the battery using the data calculated above; Mode-saving and storing the calculated battery energy consumption history; The discharged voltage is changed to the latest one, including time increment, and continuously calculated and calculated until the battery voltage falls below the cutout point or reaches a limit set value by the user. A mileage prediction and extension method for an electric vehicle.