KR101329915B1 - Maximum available power estimation technique of the hev lithium battery during on line driving situation and the apparatus thereof - Google Patents

Abstract

)을 산출하고, 장기 비선형 관측기에서 저항

를 산출한 다음 최대 출력을 산출하기 때문에 주행 중에도 최대 출력 파워를 정밀하게 측정할 수 있을 뿐만 아니라 배터리의 노화나 파라미터 변화에 대해서도 고정밀도의 제어 성능을 보여줄 수 있는 효과가 있다.Disclosed are a method and an apparatus for predicting maximum output power while driving a battery for a hybrid electric vehicle. According to the present invention, the amount of charge in a short-term nonlinear observer using information of battery voltage, current, and temperature input even while driving a vehicle (

) And resistance in long-term nonlinear observers

Since the maximum output power is calculated, the maximum output power can be precisely measured even while driving, and high accuracy control performance can be shown even with aging or parameter change of the battery.

Description

MAXIMUM AVAILABLE POWER ESTIMATION TECHNIQUE OF THE HEV LITHIUM BATTERY DURING ON LINE DRIVING SITUATION AND THE APPARATUS THEREOF}

The present invention relates to a method for estimating the maximum output power of a battery for a hybrid electric vehicle (HEV), and more particularly, the maximum output power according to various environmental variables affecting the maximum output power of a battery mounted in a vehicle. The present invention relates to a method of estimating the maximum output power during driving of a hybrid electric vehicle battery that can accurately estimate and improve a vehicle's performance and fuel economy.

2. Description of the Related Art [0002] An electric vehicle uses a lithium-ion battery, which is a secondary battery, as a driving fuel, drives a power generating device by a power source output from the battery, transmits it to a driving wheel through a power transmitting device, Thereby driving the automobile.

In the secondary battery, as the discharge proceeds, the terminal voltage between the positive electrode and the negative electrode gradually decreases and reaches a certain limit, and then rapidly decreases to reach the discharge end voltage, and thereafter, the discharge capacity is lost. When discharge is performed up to the discharge termination voltage, the electrode plate which causes a chemical reaction with the electrolytic solution and generates a current is damaged, and the function as a storage battery is lost.

Accordingly, the electric vehicle can travel as much as the capacity charged in the battery. The electric vehicle can be used while reversing the power generated by reversing the rotational power of the driving wheel during driving, and the vehicle is operated until the battery is completely discharged It is difficult to recharge the battery, so it is important to accurately grasp the state of charge (SOC) of the battery during running.

SOC is nonlinear because it is determined by the chemical state inside the cell. Thus, SOC cannot be directly and accurately measured by an electrical signal, but indirectly predicted by physical measurements such as voltage across the battery, input current, and the like. In particular, large-capacity secondary batteries used for hybrid electric electric vehicles or electric vehicles having high C-rate are known to be difficult to accurately predict SOC because of their large nonlinearity.

In general, a battery applied to a hybrid vehicle undergoes rapid charging and discharging due to rapid acceleration and sudden deceleration that frequently occur when the vehicle is driven. Due to such rapid charging and discharging, the state of charge and the maximum output of the battery show a dynamic behavior with a large change rate. In addition, the maximum output of the battery shows a great change with temperature. Due to these various variables, it is very difficult to accurately predict the maximum output of the battery while the vehicle is running.

In the past, as a method of checking the remaining capacity of a battery, a method of finding the maximum output to voltage using only voltage was used.

The disadvantage of this method is that it is not possible to detect the power loss due to aging due to the use of batteries.

There are techniques for detecting battery decay (capacity, resistance), but all of the existing techniques are available in the off-line state – when the car is not driving.

Since the conventional method obtains the maximum output information based on the battery voltage, the accuracy is degraded due to temperature or other battery internal state change, and it does not detect the change of the battery parameter occurring while driving, and the battery type is changed or the size is changed. The problem is that the parameters need to be adjusted again.

An object of the present invention to solve this problem is to provide a method for predicting the maximum output power during the running of the battery for a hybrid electric vehicle using a model-based battery state equation.

In addition, the present invention is a method of predicting the maximum output power during the driving of the hybrid electric vehicle battery consisting of a sliding mode observer that is a non-linear observer that can determine the internal state variables of the battery using the battery voltage, current, temperature information input while driving the vehicle. To provide another purpose.

In addition, the present invention obtains the amount of charge (Z) at the output of the short-term nonlinear observer, and obtains the resistance (R) at the output of the long-term nonlinear observer, the maximum output during the running of the hybrid electric vehicle battery that can calculate the maximum output in the maximum output calculator It is another object to provide a power prediction method.

,

및

를 계산하는 수학적 알고리즘으로 충전량(

)을 산출하는 단기 비선형 관측기와, 오프-라인 상태에서의 정 전류 방전 실험을 토대로 한 2차 전지의 모델링 회로를 기준으로 저항

을 산출하는 장기 비선형 관측기 및 상기 단기 비선형 관측기의 충전량(

)과 상기 장기 비선형 관측기의 저항(

)으로 최대 출력을 산출하는 최대 출력 연산기를 포함하여 이루어질 수 있다.Induced by RC circuit modeling considering the open circuit voltage (Voc (Z)) of the secondary battery of the present invention, the capacitor component (Cp) due to the polarization effect of the battery, and the resistance component inside the battery for achieving the above object. Parameters of each state equation for the secondary battery output voltage (Vt), the voltage across the capacitor (Vp), and the SOC (Z), the positive feedback gain constant of the sliding observer designed sliding mode based on each state equation, and the open circuit voltage. The maximum output power prediction device while driving the hybrid electric vehicle battery using a separate SOC table includes prediction values of Vt, Vp, and Z based on the observer.

,

And

A mathematical algorithm for calculating the amount of charge (

Resistance based on a short-term non-linear observer that calculates) and a secondary battery modeling circuit based on a constant current discharge experiment in the off-line state.

The charge amount of the long-term nonlinear observer and the short-term nonlinear

) And the resistance of the long-term nonlinear observer (

It can be made by including a maximum output calculator for calculating the maximum output.

)을 산출하는 단계, 장기 비선형 관측기에서 오프-라인 상태에서의 정 전류 방전 실험을 토대로 한 2차 전지의 모델링 회로를 기준으로 저항

을 산출하는 단계 및 최대 출력 연산기에서 상기 충전량(

)과 상기 저항(

)을 사용하여 최대 출력을 산출하는 단계를 포함하여 이루어질 수 있다.Meanwhile, the method for predicting the maximum output power while driving the hybrid electric vehicle battery according to the present invention includes a battery parameter including any one of battery voltage, current, and temperature information, and a short-term nonlinear state variable related to voltage and a long-term nonlinear state related to output. The short-term nonlinear state variable in the short-term nonlinear observer by using a variable step and an estimate of the secondary battery output voltage (Vt), the voltage across the capacitor (Vp), and the SOC (Z).

), The resistance based on the modeling circuit of the secondary battery based on the constant current discharge experiment in the off-line state in the long-term nonlinear observer

Calculating the amount of charge in the maximum output calculator (

) And the resistance (

) To calculate the maximum output.

In the above, the short-term nonlinear observer is characterized in that each state equation for the secondary battery output voltage (Vt), the voltage across the capacitor (Vp) and SOC (Z) is represented by the following equation.

이고 ,

이며,

이다.here,

ego ,

Lt;

to be.

In addition, the short-term nonlinear observer for the secondary battery output voltage (Vt), the voltage across the capacitor (Vp) and SOC (Z) is expressed by the following equation.

,

,

는 정 궤환 이득 상수이다.here,

,

,

Is the positive feedback gain constant.

과

가 다음의 수학식에 의하여 산출된다.In addition, long-term nonlinear observers can be used for modeling circuitry in secondary cells.

and

Is calculated by the following equation.

는

의 예측치이고,

은

의 예측치이다.here,

The

Is an estimate of,

silver

Is an estimate of.

The maximum output calculator is configured to calculate the maximum output using the following equation.

는 충전량에 해당되는 개방 전압으로 배터리 충방전 시험에 의해서 미리 얻어지는 상수이며, V_limit은 충전과 방전에 따라 결정되는 전압치이다.
here

Is an open voltage corresponding to the charge amount, and is a constant obtained by a battery charge / discharge test in advance, and V _limit is a voltage value determined by charge and discharge.

Therefore, according to the method of predicting the maximum output power while driving the hybrid electric vehicle battery of the present invention, the maximum output power can be precisely measured by using the battery voltage, current, and temperature information input while driving the vehicle.

In addition, according to the present invention, there is an effect that can show a high-precision control performance even with the aging of the battery and the change of parameters.

1 is a main configuration diagram of a battery electric propulsion system of a hybrid vehicle using a method of predicting the maximum output power during the driving of the battery according to an embodiment of the present invention,
2 is a configuration diagram of a maximum output predictor of a battery pack system;
3 is a mathematical model estimation diagram of a maximum output predictor,
4 is an OCV (Z) curve of a lithium battery,
5 is a modeling circuit diagram of a secondary battery according to a preferred embodiment of the present invention;
And,
FIG. 6 is a graph in which open circuit voltages are plotted over an entire period of state of charge (SOC) (0 to 1) using the result of a constant current discharge experiment according to the present invention.

It is to be understood that the words or words used in the present specification and claims are not to be construed in a conventional or dictionary sense and that the inventor can properly define the concept of a term in order to describe its invention in the best possible way And should be construed in light of the meanings and concepts consistent with the technical idea of the present invention.

Throughout the specification, when an element is referred to as "comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the terms " part, "" module, "and" device "Lt; / RTI >

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

FIG. 1 is a main configuration diagram of a battery propulsion system of a hybrid vehicle capable of predicting a maximum output power of a battery according to an embodiment of the present invention, and FIG. 2 is a battery pack system 100 of the present invention. As shown in the figure, the maximum output (Pmax) of the battery from the battery pack system 100 and the inverter status information from the inverter 210 in the HCU (Hybrid Control Unit) 230, the controller of the vehicle, And it is configured to receive the motor torque and RPM information, etc. from the motor 220 to determine the output of the desired motor.

Inverter 210 is configured to stabilize the power supply to the motor 220, when power is applied from the battery 120 to drive the vehicle, it is configured to output the output energy to the motor 220 and the corresponding state And to transmit to HCU 230.

The battery pack system 100 is configured to supply and charge power for driving the vehicle to the lithium battery 120, detect the voltage, current, and temperature information of the battery 120 while driving to calculate the maximum output to calculate the HCU ( 230).

The battery pack system 100 of the present invention receives a charge / discharge current value given from the inverter 210 and a battery voltage of the lithium battery 120 to calculate a charge amount Z, and a short-time sliding mode observer. And a long-time sliding mode observer 114 that calculates the resistance R. The maximum output calculator 116 receives the maximum amount of charge Z and the resistance R. the maximum power (P _max) calculated after calculating the (P _max) is constructed in a structure for transmitting to the HCU (230).

Hereinafter, a process of calculating the maximum output while driving in the battery pack system according to an exemplary embodiment of the present disclosure will be described with reference to FIG. 2.

According to the present invention, a battery parameter is composed of a short-time state variable related to a voltage and a long-time state variable related to an output using a model-based battery state equation, and is inputted while driving a vehicle. In order to determine the internal state variables of the battery by using the battery voltage, current, and temperature information, a nonlinear sliding observer was constructed.

That is, a short-time sliding mode observer 112 for finding short-time state variables and a long-time sliding mode for finding long-time state variables. observer 114).

In addition, since the time constants of the state variables are different, the short-term nonlinear observer 112 is used in the inner loop of the battery controller 110 and the long-term nonlinear observer 114 is used in the outer loop of the battery controller 110 so that the inner loop and the outer It is configured to exchange information about the state variables that are updated in the loop.

Maximum output Pmax available at the maximum output operator 116 using the variable for charge amount Z, which is the output of the short-term nonlinear observer 112, and the variable for resistance R, which is the output of the long-term nonlinear observer 114. Will be calculated.

Hereinafter, a method of estimating the maximum output P _max of the present invention using the mathematical model estimation diagram of the maximum output predictor of FIG. 3 will be described.

First, a sliding mode observer which is a theoretical background in the present invention will be described.

First, a continuous-time single input system and a scalar measurement model mathematically modeled by Equations 1 and 2 will be described.

는 모델링된 시스템의 시간 t에서의 상태(state variable)이고,

는 시간 t에서의 스칼라 궤환 제어에 따른 시스템 제어 입력이고,

는 시간 t에서의 경계가 있는(bounded) 오차 및 왜란(disturbance)을 정량화한 인자이고,

는 시간 t에서의 시스템 출력이다.From here,

Is the state variable at time t of the modeled system,

Is the system control input according to scalar feedback control at time t,

Is a factor that quantifies the bounded error and disturbance at time t,

Is the system output at time t.

에 대한 슬라이딩 모드 관측기는 다음 수학식 3과 같이 정의된다. 수학식 3에서,

은 각각

의 예측치(estimate)이다.In the system modeled by Equations 1 and 2, states

The sliding mode observer for is defined as in Equation 3 below. In Equation (3)

Respectively

Is an estimate of.

이고,

이고,

는 시그넘(signum)함수이고,

은 스위칭 궤환 이득 상수이다.here,

ego,

ego,

Is a signum function,

Is the switching feedback gain constant.

는 하기 수학식 4와 동적 특성을 갖는다.Error in Sliding Mode Observer

Has a dynamic characteristic with the following equation (4).

이고, 스위칭 함수

는

이다.here,

Switching function

The

to be.

이라는 조건이 성립되면 국소적인 슬라이딩 레자임(regime)은 시스템 상태 공간 내의

이라는 평면상에 있게 된다.On the other hand, according to the sliding mode theory,

If a condition is established, the local sliding regime is defined in the system state space.

Is on the plane of.

는 하기 수학식 5로 나타낼 수 있다. 수학식 5에서 T는 전치(transpose) 행렬을 의미한다.Meanwhile,

Can be represented by Equation 5 below. In Equation 5, T means a transpose matrix.

이 0보다 크면, 국소적인 슬라이딩 레자임은 조건In Equation (5)

If this is greater than zero, the local sliding regime is a condition

하에서

라는 평면 위에 존재하게 된다. 이때 집합

는 슬라이딩 패치(patch)라고 불린다.

Under

Will exist on the plane. At this time

Is called a sliding patch.

는 'Filippov'의 근(solution)에 의해 결정된다.On the other hand, in the ideal sliding mode dynamics,

Is determined by the solution of 'Filippov'.

에 대하여,That is,

about,

이고,

이다.

ego,

to be.

는 스위칭 함수를

의 스팬(span)을 따라

의 널 공간(null space)으로 투영했을 때의 행렬이다.here,

The switching function

Along the span of

The matrix when projecting into null space.

The theoretical background of the sliding mode observer described above will be referred to in an embodiment of the present invention described below. However, it will be apparent to those skilled in the art that various well-known sliding mode observer theories may be applied in addition to the theories presented herein.

Next, a modeling circuit of the secondary battery employed in the present invention will be described in order to predict the SOC of the secondary battery according to a preferred embodiment of the present invention.

In general, the output voltage of the secondary battery has a characteristic of changing nonlinearly with respect to the SOC of the battery. Such nonlinear characteristics can be easily confirmed by measuring an open circuit voltage of a secondary battery while periodically charging a secondary battery and then periodically discharging a certain amount of current.

In the present invention, the secondary battery can be dynamically modeled by the RC circuit model shown in FIG. 5 in view of the nonlinear characteristics of the secondary battery.

Referring to FIG. 5, a modeling circuit for a secondary battery includes an open circuit voltage V _oc (Z) (reference is Z is SOC) that exhibits nonlinear characteristics, and a capacitor component for modeling polarization effects in the battery. C _p , the ripple component R _b to model propagation resistance, the diffusion resistance component R _{p as} a function of current (I), the ohmic resistance component R _t, and the output voltage V _t of the secondary battery. It includes.

The output voltage V _t of the secondary battery can be expressed by the following equations (6) and (7). In Equations 6 and 7, I is instantaneous current. Instantaneous current has a positive value for charging and a negative value for discharging.

On the other hand, the time derivative of SOC Z of the secondary battery is expressed by Equation 8 below.

는 개방 회로 전압의 전류이고

는 2차 전지의 공칭 정전용량(nominal capacitance)이다.here,

Is the current of the open circuit voltage

Is the nominal capacitance of the secondary battery.

Since the left sides of Equations 6 and 7 are identical to each other, the following Equation 9 can be derived through a simple algebra operation.

By applying the Kirchoff's law of Equation 10 to Equation 9, Equation 11 can be obtained.

Substituting Equation 11 into Equation 8 provides Equation 12 below.

By substituting Equation 12 into Equation 10 in a similar manner, Equation 13 is obtained.

는 수학식 11의

를 수학식 7에 대입하면 하기 수학식 14와 같이 얻을 수 있다.Output voltage of secondary battery

Of equation (11)

By substituting into Equation 7, it can be obtained as Equation 14 below.

이라는 조건을 적용하여 정리하면, 하기 수학식 15가 얻어진다.Time differential the output voltage according to Equation 14,

If the following condition is applied and summarized, the following formula (15) is obtained.

에 대하여 풀고 그 결과를 수학식 14에 대입하면, 하기 수학식 16과 같이

,

및

에 대한 완전한 상태 방정식을 얻을 수 있다. 이로써, 본 발명에 따른 2차 전지의 회로 모델링이 완료된다.Finally, Equation 6

Solving for and substituting the result into Equation 14,

,

And

You can get the complete state equation for. This completes the circuit modeling of the secondary battery according to the present invention.

,

,

,

,

,

,

는 다음과 같다.here,

,

,

,

,

,

,

Is as follows.

,

,

,

,

,

,

의 계산을 위해 사용되는 2차 전지의 상수값

,

,

,

,

는 2차 전지를 만 충전시킨 후 주기적으로 정 전류를 방전시키면서 개방 회로 전압을 측정한 후 그 실험 결과와 매칭될 수 있도록 시행착오법에 의해 상기 상수값들을 결정한다.In the above state equation, the parameter

,

,

,

,

,

,

Constant value of secondary battery used for calculation of

,

,

,

,

After the battery is charged only, the open circuit voltage is measured while periodically discharging a constant current, and the constant values are determined by a trial and error method so as to match the experimental result.

The design of the short-term nonlinear observer 112 in accordance with the preferred embodiment of the present invention is based on a state equation according to (16).

Specifically, the rank of the observability matrix of Equation 16 is full rank. Therefore, the internal state of the modeled secondary battery can be predicted by the sliding mode observer.

For reference, the observability matrix is [C CA CAA].

이다.Where C is (1 0 0) and A is

to be.

에 대한 상태 방정식에 적용하면,

에 대한 슬라이딩 모드 관측기는 하기 수학식 17과 같이 나타낼 수 있다.In the present invention, the design of the sliding mode observer starts from the equations (3) and (5) described above. Specifically, Equation 3 is expressed in Equation 16

Applying to the state equation for,

The sliding mode observer for may be represented by Equation 17 below.

은

의 예측치이고,

은 정 궤환 이득 상수(positive feedback gain constant)이다.here,

silver

Is an estimate of,

Is the positive feedback gain constant.

를

로 정의하면, 하기 수학식 18과 같은 오차 방정식을 얻을 수 있다.Estimation and Actual Error of System Output

To

If defined as, the error equation shown in Equation 18 can be obtained.

Where sgn (e _y ) is

이 충분히 크면

에 의해

의 부호가 결정된다. 그리고

에 의해

와

의 부호는 항상 반대가 된다. 그 결과,

값에 상관없이

에 슬라이딩 모드 운동이 유발됨으로써 일정한 시간이 흐른 뒤에는

및

가 0으로 수렴한다. Referring to Equation 18,

If this is big enough

By

The sign of is determined. And

By

Wow

Is always reversed. As a result,

Regardless of the value

After a certain period of time,

And

Converges to zero.

은 시행착오법(trial and error)에 의해 결정된다.Where the positive feedback gain constant

Is determined by trial and error.

가 그 등가 치인

로 치환된 것과 같은 양상을 보인다. 이때,

는 수학식 18에서

및

가 0이라고 가정하여 구하므로

와

사이에는 하기 수학식 19와 같은 관계가 성립한다.Meanwhile, according to the equivalent control method, the error system in the sliding mode

That is equally hit

It shows the same aspect as substituted with. At this time,

In Equation 18

And

Is assumed to be 0, so

Wow

The following relationship holds for the following equation (19).

The design of the sliding mode observer for the output voltage V _t of the secondary battery is completed by derivation of the relation according to Equation 19.

에 대한 슬라이딩 모드 관측기 설계 방법을 설명한다.Next SOC

A sliding mode observer design method is described.

에 대한 상태 방정식에 적용하면,

에 대한 슬라이딩 모드 관측기는 하기 수학식 20과 같이 나타낼 수 있다.Specifically, Equation 3 is replaced by Equation 16

Applying to the state equation for,

The sliding mode observer for may be represented by Equation 20 below.

은

의 예측치이고, 정 궤환 이득 상수이다.here,

silver

Is the predicted value and is the positive feedback gain constant.

및

의 오차

및

를 각각

및

로 정의하면, 하기 수학식 21과 같은 오차 방정식을 얻을 수 있다.SOC

And

Error

And

Respectively

And

If defined as, an error equation such as the following Equation 21 can be obtained.

는 다음과 같다.here,

Is as follows.

는 비선형적이긴 하지만 대체로 SOC

에 비례한다. 따라서

는 하기 수학식 22와 같이 구간 구간 별로

에 비례한다고 근사할 수 있다.Open circuit voltage

Is non-linear, but usually SOC

Proportional to therefore

Is for each section as shown in Equation 22 below.

It can be approximated to be proportional to.

Therefore, Equation 21 may be arranged as Equation 23 below.

의 오차 방정식(수학식 18)과 마찬가지로,

가 충분히 크면

및

의 부호는 항상 반대가 되므로

에 슬라이딩 모드 운동이 유발됨으로써 일정한 시간이 흐른 뒤에는

및

모두가 0으로 수렴한다. 여기서, 상기 정 궤환 이득 상수

는

과 마찬가지로 시행착오법에 의해 결정된다.Referring to Equation 23,

As with the error equation (Equation 18),

Is large enough

And

Is always reversed.

After a certain period of time,

And

All converge to zero. Where the positive feedback gain constant

The

Similarly, it is determined by the trial and error method.

에 대한 슬라이딩 모드 관측기의 설계시와 마찬가지로 등가 제어 방법론을 적용하면, 하기 수학식 24와 같은 관계식을 도출할 수 있다.Also

By applying the equivalent control methodology as in the design of the sliding mode observer for the relation, it is possible to derive the relational expression as shown in Equation (24).

에 대한 슬라이딩 모드 관측기의 설계가 완료된다.If the relation according to Equation 24 is derived, SOC of the secondary battery

The design of the sliding mode observer for is completed.

에 대한 슬라이딩 모드 관측기와 오차 방정식을 설계하면 하기 수학식 25 및 26과 같다.Finally, apply the above process substantially the same

When the sliding mode observer and the error equation is designed for Equations 25 and 26.

가 충분이 크면

및

의 경우와 마찬가지로, 일정한 시간이 경과되면

및

는 0으로 수렴한다. 상기 정 궤환 이득 상수

는

과 마찬가지로 시행착오법에 의해 결정된다.In Equation 26,

Is big enough

And

As in the case of,

And

Converges to zero. The positive feedback gain constant

The

Similarly, it is determined by the trial and error method.

,

,

,

,

,

,

와, 이들 파라미터 계산에 사용되는 RC성분인

,

,

,

,

와 상태 방정식의 정 궤환 이득 상수

,

및

와, 정 전압 방전 실험을 통하여 획득한 개방 회로 전압별 SOC 값을 수록하고 있는 테이블과, 상기 파라미터; 상기 정 궤환 이득 상수; 상기 개방 회로 전압별 SOC 테이블; 전류 센서 및 전압 센서에 의해 수집되는 2차 전지(120)의 입력 전류(I) 및 출력 전압(Vt)을 이용하여 주기적으로 SOC의 예측치

를 출력하는 SOC 예측 프로그램이 수록되어 있다. 상기 저장매체는 불활성 메모리인 것이 바람직하고, 예컨대 플래쉬 메모리, ROM, EEPROM 등이 채용될 수 있는데, 본 발명이 이에 한하는 것은 아니다.In addition, the storage medium (not shown) of the short-term sliding mode observer 112 is a parameter used in the state equation of the secondary battery 120.

,

,

,

,

,

,

And the RC component used to calculate these parameters

,

,

,

,

Feedback gain constants of equations and state equations

,

And

And a table for storing SOC values for each open circuit voltage obtained through the constant voltage discharge experiment, and the parameters; The positive feedback gain constant; An SOC table for each of the open circuit voltages; Prediction of SOC periodically using input current (I) and output voltage (Vt) of secondary battery 120 collected by current sensor and voltage sensor

The SOC prediction program that outputs is shown. Preferably, the storage medium is an inert memory. For example, a flash memory, a ROM, an EEPROM, or the like may be employed, but the present invention is not limited thereto.

The battery controller 110 controls the overall operation of the short-term nonlinear observer 112. The battery controller 110 executes the SOC prediction program stored in the storage medium to proceed with the initialization of the short-term nonlinear observer 112 when power is applied, and the parameter value of the secondary battery state equation (see Equation 16). Load the positive feedback gain constant into memory and store initial current and voltage data in memory.

Here, the initial currents and voltages refer to currents and voltage levels measured just before starting of the vehicle on which the short-term nonlinear observer 112 is mounted (before ignition starts). Therefore, the initial current is 0, and the initial voltage has a predetermined value according to the charge amount of the lithium battery 120 which is the secondary battery. In this case, the initial voltage is regarded as an initial open circuit voltage of the lithium battery 120 which is a secondary battery for convenience.

,

및

를 계산하여 내부 메모리에 누적적으로 저장하는 한편, SOC의 예측치

를 외부로 출력한다.Meanwhile, the battery controller 110 reads the SOC value corresponding to the initial voltage value by referring to the SOC table for each open circuit voltage stored in the internal memory. Then periodically

,

And

Is computed and stored cumulatively in internal memory, while predicting SOC

To the outside.

,

및

를 계산한다.Specifically, in the present invention, by applying the above-described equivalent control methodology to the equation 17, 20 and 25 of the sliding mode observation equation of the lithium battery 120 as a secondary battery, the sliding mode observation equation is Euler discrete as shown in Equation 27 Type sampling time Ts

,

And

.

,

및

의 구체적인 계산 방식은 다양하게 변형이 가능하며, 본 발명의 기술적 범위가

,

및

의 구체적인 계산 방식에 의해 한정되지 않음은 당연하다.In an embodiment of the invention, the sampling time Ts is 1 second. On the other hand, the size of the sampling time,

,

And

The specific calculation method of the can be variously modified, the technical scope of the present invention

,

And

Of course, it is not limited by the specific calculation method of.

,

및

를 계산하기 위해 상기 수학식 27의 초기 조건을 다음과 같이 설정한다.The battery controller 110 of the secondary battery 120

,

And

In order to calculate the initial condition of Equation 27 is set as follows.

은 차량의 시동 전에 측정된 2차 전지(B)의 초기 전압 레벨로,

은 차량의 시동 전에 측정된

를 개방 회로 전압

로 간주하여 메모리에 저장된 개방 회로 전압별 SOC 테이블을 참조하여 얻은 2차 전지(120)의 초기 SOC 값으로,

은 차량의 시동 전에 흐르는 전류 I가 0이라는 점을 감안하여

과 동일한 값으로,

;

;

는 계산상의 편의를 위해

, 0,

으로 초기 상태를 설정한다.In other words,

Is the initial voltage level of the secondary battery (B) measured before starting of the vehicle,

Is measured before the vehicle starts up.

Open circuit voltage

Initial SOC value of the secondary battery 120 obtained by referring to the SOC table for each open circuit voltage stored in the memory,

Considering that the current I flowing before starting the vehicle is zero.

Is the same value as

;

;

For convenience of calculation

, 0,

Set the initial state with.

;

;

를 위와 같이 설정해도 관측기의 성능에 유의적인 영향을 미치지 않는다.Initial conditions due to the nature of the short-term sliding mode observer 112

;

;

Setting as above does not significantly affect the performance of the observer.

,

,

을 계산하여 내부 메모리에 저장하고

는 외부로 출력한다.When the initial state is set, the battery controller 110 applies the initial condition as described above to Equation 27 before the next sampling time (t = 2 seconds) is reached, and predicts the value at t = 2 seconds.

,

,

Calculate and store in internal memory

Outputs to the outside.

,

,

와 샘플링 타임(t=2초)에서 측정된

와 I, 개방 회로 전압별 SOC 테이블에서

에 상응하는

에 의하여

,

,

을 계산하고, 계산된

는 최대 출력 연산기(116)로 출력하는 것이다.After that, when the next time sampling time (t = 2 seconds) is reached, the battery controller 110 is stored in the memory.

,

,

And measured at sampling time (t = 2 seconds)

And I, in the SOC table by open circuit voltage

Equivalent

By

,

,

Is calculated,

Is output to the maximum output operator 116.

,

및

에 대한 계산 과정은 샘플링 주기 Ts가 경과될 때마다 반복적으로 이루어진다.Lithium battery 120 of the secondary battery as described above

,

And

The calculation process for is repeated every time the sampling period Ts elapses.

는 최대 출력 연산기(116)로 입력된다. Preferably, the prediction of the SOC output through the short term sliding mode observer 112

Is input to the maximum output operator 116.

에 의해 2차 전지(120)의 내부 상태를 판정하고, 만약 충전이 필요한 경우(예컨대 값이 소정 레벨 이하로 떨어진 경우)는 전정류/정전압 모드에서 2차 전지(120)를 충전시킨다. 최대 출력 연산기(116)에 의한 2차 전지 충전 동작은 본 발명이 속한 기술분야에서 널리 알려진 기술이므로 여기에서의 상세한 설명은 생략하기로 한다The maximum output operator 116 then estimates the SOC

The internal state of the secondary battery 120 is determined, and if the charging is necessary (for example, when the value falls below a predetermined level), the secondary battery 120 is charged in the pre-constant / constant voltage mode. Since the secondary battery charging operation by the maximum output calculator 116 is well known in the art to which the present invention pertains, a detailed description thereof will be omitted.

,

,

,

,

,

,

와, 이들 파라미터 계산에 사용되는 RC성분인

,

,

,

,

는 상수일 수도 있지만, 2차 전지(120)의 입력 전류 및/또는 SOC 에 의해 변화될 수도 있다. 이러한 경우, 상기 저장매체는 입력 전류 및/또는 SOC의 변화에 따라 실험적으로 결정된 RC 성분 값들의 테이블을 추가로 수록하고 있을 수 있다. 그러면 배터리제어기(110)는 샘플링 시간 Ts가 경과될 때마다 저장매체에 수록된 RC성분들의 테이블을 참조하여 파라미터

,

,

,

,

,

,

의 계산을 실시간으로 수행할 수 있다.On the other hand,

,

,

,

,

,

,

And the RC component used to calculate these parameters

,

,

,

,

May be a constant, but may be changed by the input current and / or the SOC of the secondary battery 120. In this case, the storage medium may further include a table of RC component values experimentally determined according to the change of the input current and / or SOC. Then, whenever the sampling time Ts elapses, the battery controller 110 refers to a table of RC components contained in the storage medium.

,

,

,

,

,

,

Can be calculated in real time.

)는 최대 출력 연산기(116)로 입력됨과 동시에 충전량 예측치(

)는 충전량 동적 평균화부(Moving Average)(115a)에 입력되어 충전량의 동적 평균치인

가 산출되어 장기 비선형 관측기(Long-time sliding mode observer)(114)로 궤환된다.In addition, the charge prediction value calculated by the short-time sliding mode observer 112 (

) Is input to the maximum output calculator 116 and at the same time the charge estimate (

) Is input to the moving amount moving average 115a, which is a dynamic average value of the filling amount.

Is calculated and fed back to a long-time sliding mode observer 114.

)는 최대 출력 연산기(116)로 입력됨과 동시에 저항 예측치(

)는 저항 동적 평균화부(Moving Average)(115b)에 입력되어 저항의 동적 평균치가 산출되어 단기 비선형 관측기(short-time sliding mode observer)(112)로 궤환된다.And the resistance prediction value calculated by the long-time sliding mode observer 114.

) Is input to the maximum output operator 116 and at the same time the resistance prediction value (

) Is input to a moving average 115b to calculate a dynamic average of the resistance and is fed back to the short-time sliding mode observer 112.

,

,

,

,

,

,

의 계산을 위해 사용되는 2차 전지의 상수값 중 일부인

과

는 2차 전지를 만 충전시킨 후 주기적으로 정 전류를 방전시키면서 개방 회로 전압을 측정한 후 그 실험 결과와 매칭될 수 있도록 시행착오법에 의해 결정된다.Meanwhile, in the long-time sliding mode observer 114, a defined state function that is a parameter used in the state equation of Equation 16 is used.

,

,

,

,

,

,

Some of the constant values of secondary batteries used to calculate

and

Is determined by trial and error method to measure the open circuit voltage while periodically discharging a constant current and then to match the experimental results.

,

,

,

,

,

,

를 결정하고 전지의 개방 회로 전압을 SOC의 전 구간(0 ~ 1)에 대해 설정하기 위한 목적으로 2차 전지를 4.2V까지 완충전시킨 후 정 전류 방전 실험을 수행한다. 정 전류 방전 실험은 180초 동안 5A의 전류를 방전시켰다가 3600초 동안 방전을 휴지하는 과정을 하나의 싸이클로 하여 20회 반복한다.One of these methods is the parameter of the sliding mode observer.

,

,

,

,

,

,

After the secondary battery is fully charged to 4.2V for the purpose of determining and setting the open circuit voltage of the battery for the entire interval (0 to 1) of the SOC, a constant current discharge experiment is performed. In the constant current discharge experiment, the cycle of discharging 5A for 180 seconds and stopping the discharge for 3600 seconds is repeated 20 times as one cycle.

For reference, discharging a current of 5A for 180 seconds corresponds to a '1-C rate' of nominal capacity 5.0Ah. In the constant current discharge experiment, when the current of 5A is discharged, the SOC of 5% is reduced. In the constant current discharge experiment, the present inventors obtained a graph showing the correlation between the SOC and the open circuit voltage as shown in FIG. 6 by measuring the open circuit voltage at one second intervals during the current discharge.

,

를 산출하였다.Then, based on the constant current discharge experiment in the off-line state as described above for the modeling circuit of the secondary battery employed in the present invention

,

Was calculated.

를 산출하게 된다.After all, in the long-term nonlinear observer 114 by the following equation

Will yield.

는

의 예측치이다.here

The

Is an estimate of.

는 최대 출력 연산기(116)로 입력된다.Calculated as above

Is input to the maximum output operator 116.

Thereafter, the maximum output calculator 116 that receives the variable for the charge amount Z and the variable for the resistance R calculates the maximum output P _max using these two variables.

)와 장기 비선형 관측기(114)에서 산출된 저항(

)을 이용하여 먼저 최대 전류(I_max)를 수학식 28에 의하여 산출하고, 산출된 최대 전류(I_max)로부터 수학식 29에 의하여 최대 출력(P_max)을 계산하게 된다.That is, the charge prediction value calculated by the short-term nonlinear observer 112 (

) And the resistance calculated by the long-term nonlinear observer 114 (

First, the maximum current I _max is calculated by Equation 28, and the maximum output P _max is calculated by Equation 29 from the calculated maximum current I _max .

는 충전량에 해당되는 개방 전압으로 배터리 충방전 시험에 의해서 미리 얻어진다. 도 6에 리륨 배터리의 OCV(Z) 커브가 나타나 있다.here

Is obtained in advance by a battery charge / discharge test with an open voltage corresponding to the charge amount. 6 shows an OCV (Z) curve of a lithium battery.

FIG. 6 is a graph in which open circuit voltages are plotted over an entire period of state of charge (SOC) (0 to 1) using the result of a constant current discharge experiment according to the present invention.

The V _limit is determined by charging and discharging. Generally, 4.2V is charged and 2.8V is discharged.

The calculated P _max is also expressed as P _chg , max, P _dis , and max because the charge and discharge values are different.

As described above, the output of the battery is previously extracted by a sliding mode observer, which is a nonlinear observer that can determine the internal state variables of the battery by using battery voltage, current, and temperature information that affects the output of the battery while driving. The maximum output is calculated using the relationship, and the calculated maximum output value is transmitted to the vehicle control apparatus of the HCU 230 of the hybrid electric vehicle to control the output of the battery.

In the present invention, the experiment for verifying the accuracy of the battery maximum power estimation method was carried out, it was possible to obtain the OCV (Z) curve of the lithium battery as shown in FIG.

Since the battery's state of charge (SOC) shows that the OCV (Z) cover by temperature at multiple temperatures (temp) is nearly identical, the maximum output of the battery can be estimated regardless of the temperature difference. Can be.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art.

100: battery pack system 110: battery controller
112: short-term nonlinear observer 114: long-term nonlinear observer
116: maximum output calculator 120: secondary battery
210: inverter 220: motor
230: HCU

Claims (9)

Secondary battery output voltage (Vt) induced by RC circuit modeling taking into account the open circuit voltage (Voc (Z)) of the secondary battery, the capacitor component (Cp) due to the polarization effect of the battery, and the resistance component inside the battery, Hybrid electric vehicle battery using the parameters of the state equations for the voltage across the capacitor (Vp) and SOC (Z), the positive feedback gain constant of the sliding observer designed in the sliding mode based on the above state equations, and the SOC table for each open circuit voltage. In the maximum output power prediction device while driving of,
Estimates of Vt, Vp and Z based on the observer

,

And

A mathematical algorithm for calculating the amount of charge (

A short-term nonlinear observer yielding;
Resistance based on the modeling circuit of a secondary battery based on experiments with constant current discharge in off-line state

A long-term nonlinear observer that yields a; and
The charge amount of the short-term nonlinear observer (

) And the resistance of the long-term nonlinear observer (

A maximum output calculator for calculating a maximum output with;
Including,
The short-term nonlinear observer
Each state equation for the secondary battery output voltage (Vt), the capacitor voltage across the capacitor (Vp) and SOC (Z) is expressed by the following equation, the maximum output power during the driving of the hybrid electric vehicle battery Prediction device.

here,

ego ,

this
In addition,

Lt;
Cn is the nominal capacitance of the secondary battery,
I is the instantaneous current of the modeling circuit for the secondary battery,
Rb is a propagation resistance component for modeling propagation resistance of a modeling circuit for a secondary battery,
Rp is a diffusion resistance component that is a function of the current (I) of the modeling circuit for the secondary cell,
Rt is the ohmic resistance component of the modeling circuit for the secondary battery.
delete The method of claim 1,
The short-term nonlinear observer for the secondary battery output voltage (Vt), the capacitor voltage across the capacitor (Vp), and the SOC (Z) is expressed by the following equation. Device.

here,

,

,

Is the positive feedback gain constant.
The method of claim 1,
The long term nonlinear observer
For the modeling circuit of the secondary battery

and

The maximum output power prediction device during driving of the hybrid electric vehicle battery, characterized in that it is calculated by the following equation.

here,

The

Is an estimate of,

silver

Is an estimate of
e _x is the error between the predicted and actual values of the system output for X. The method of claim 1,
The maximum output calculator is a maximum output power prediction device while driving the hybrid electric vehicle battery, characterized in that for calculating the maximum output by the following equation.

here

Is an open voltage corresponding to the charge amount, and is a constant obtained by a battery charge / discharge test in advance, and V _limit is a voltage value determined by charge and discharge.
Constructing a battery parameter composed of any one of battery voltage, current, and temperature information into a short-term nonlinear state variable related to a voltage and a long-term nonlinear state variable related to an output;
Using the estimates of the secondary battery output voltage (Vt), the voltage across the capacitor (Vp), and the SOC (Z), the amount of charge

);
Resistance based on modeling circuit of secondary battery based on constant current discharge experiment in off-line state in long-term nonlinear observer

Calculating a; and
The charge amount at the maximum output calculator (

) And the resistance (

Calculating a maximum output using;
Including,
The short-term nonlinear observer for the secondary battery output voltage (Vt), the voltage across the capacitor (Vp), and the SOC (Z) is expressed by the following equation. Way.

here,

,

,

Is the positive feedback gain constant,
.

ego ,

this
In addition,

Lt;
Cn is the nominal capacitance of the secondary battery,
I is the instantaneous current of the modeling circuit for the secondary battery,
Rb is a propagation resistance component for modeling propagation resistance of a modeling circuit for a secondary battery,
Rp is a diffusion resistance component that is a function of the current (I) of the modeling circuit for the secondary cell,
Rt is the ohmic resistance component of the modeling circuit for the secondary battery.
delete The method according to claim 6,
The long term nonlinear observer
For the modeling circuit of the secondary battery

and

The maximum output power prediction method of driving a hybrid electric vehicle battery, characterized in that it is calculated by the following equation.

here,

The

Is an estimate of,

silver

Is an estimate of
e _x is the error between the predicted and actual values of the system output for X.
The method of claim 8,
The maximum output calculator is a maximum output power prediction method of driving a hybrid electric vehicle battery, characterized in that for calculating the maximum output by the following equation.

here

Is an open voltage corresponding to the charge amount, and is a constant obtained by a battery charge / discharge test in advance, and V _limit is a voltage value determined by charge and discharge.

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