KR101091664B1 - Estimation method of remained driving distance for electric vehicle - Google Patents
Estimation method of remained driving distance for electric vehicle Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60L2260/46—Control modes by self learning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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Abstract
Description
본 발명은 전기자동차 잔존 주행거리 추정방법에 관한 것으로서, 더욱 상세하게는 전기자동차 배터리의 충전 에너지에 따른 실제 주행거리 관계의 학습을 통한 잔존 주행거리 추정의 산술적 보완을 통해 잔존 주행거리에 대한 정확한 정보를 제공할 수 있는 전기자동차 잔존 주행거리 추정방법에 관한 것이다.
The present invention relates to a method for estimating the remaining mileage of an electric vehicle, and more particularly, accurate information about the remaining mileage through arithmetic complementation of the remaining mileage estimation by learning a real mileage relationship according to the charging energy of the electric vehicle battery. It relates to an electric vehicle remaining mileage estimation method that can provide.
기존의 내연기관 차량에는 내부 연료량에 따라서 주행 가능한 거리를 계산하고 표시해주는 기능이 있다. 이때, 차량마다 연료통의 크기와 연비가 다르므로 연료 눈금만으로 추가주행할 수 있는 거리에 대한 정확한 판단이 어려우며, 이에 따라 잔존 주행거리 표시기능은 매우 유용한 기능 중의 하나로 자리 잡았다.Existing internal combustion engine vehicles have a function of calculating and displaying the distance that can be driven according to the amount of internal fuel. At this time, since the size and fuel economy of the vehicle is different for each vehicle, it is difficult to accurately determine the distance that can be additionally driven only by the fuel scale, and thus the remaining mileage display function has become one of very useful functions.
최근 관심의 대상이 되고 있는 전기자동차에 있어서도, 이와 같은 잔존 주행거리 표시기능을 제공하고 있다. 종래 기술에 따른 전기자동차의 잔존 주행거리 산출방법은 고전압 배터리의 잔존에너지 즉, SOC(State of Charge)와 거리당 에너지 소모비율(km/SOC)의 관계를 이용하여 현재의 배터리 에너지 상태에서 주행 가능한 거리를 추정하는 방법을 이용하였다.Also in electric vehicles that have been the subject of interest in recent years, such a remaining mileage display function is provided. The method of calculating the remaining mileage of an electric vehicle according to the prior art is capable of driving in the current battery energy state by using the relationship between the remaining energy of the high voltage battery, that is, the state of charge (SOC) and the energy consumption ratio per distance (km / SOC). The method of estimating the distance was used.
그러나 전기자동차의 고전압 배터리는 동일한 에너지 상태, 즉 동일 SOC에서도 여러 가지 환경적 특성에 따라서 차량의 주행 가능한 거리가 달라질 수 있다. 예를 들어, 온도에 따른 배터리의 에너지 변화, 차량의 주행코스나 운전자의 운전습관에 따른 주행 가능거리의 변화, 배터리의 열화에 따른 상태 변화 등에 따라서 잔존 주행거리의 오차가 증가하고 정확도가 떨어질 수 있다.However, even in the same energy state, that is, the same SOC, an electric vehicle's high voltage battery may have a different driving distance depending on various environmental characteristics. For example, the error of the remaining mileage may increase and the accuracy may decrease according to a change in energy of the battery according to temperature, a change in driving distance of the vehicle or a driving habit of the driver, or a change in state due to deterioration of the battery. have.
도 2 내지 도 3은 이러한 전기자동차 배터리의 성능에 영향을 주는 요소들과 이에 따른 성능 변화를 나타내고 있다. 먼저, 도 2(a)는 온도에 따른 배터리의 용량 변화를, 도 2(b)는 온도에 따른 배터리 충방전 효율의 변화를, 도 2(c)는 사용량 증가에 따른 배터리 열화 현상을 나타내고 있다.2 to 3 show the factors affecting the performance of the electric vehicle battery and the performance change accordingly. First, FIG. 2 (a) shows a change in battery capacity according to temperature, FIG. 2 (b) shows a change in battery charge / discharge efficiency according to temperature, and FIG. .
도 2(a) 및 도 2(b)를 참조로 하면, 배터리는 온도에 따라서 용량과 충방전 효율이 변화하며, 일반적으로 저온일수록 용량과 효율이 감소함을 알 수 있다. 또한, 도 2(c)를 참조로 하면 배터리의 사용량과 사용시간이 증가함에 따라 열화현상이 진행하게 됨을 알 수 있는데, 열화현상에 따라 일반적으로 배터리의 용량은 감소하고 내부저항은 증가하여 효율을 감소시키는 결과를 가져온다. 이와 같이 배터리가 사용되는 환경과 누적 사용된 에너지 양, 사용한 기간에 따라서 동일 충전상태(SOC)의 배터리에서도 방전 가능한 에너지는 변화하게 됨을 알 수 있다.Referring to FIGS. 2A and 2B, the capacity and charge / discharge efficiency of the battery change according to temperature, and in general, the capacity and efficiency decrease with a low temperature. In addition, referring to Figure 2 (c) it can be seen that the deterioration phenomenon proceeds as the battery usage and use time increases, the battery capacity is generally reduced and the internal resistance increases according to the degradation phenomenon. Results in a decrease. As described above, it can be seen that the dischargeable energy is changed even in a battery having the same state of charge (SOC) according to the environment in which the battery is used, the amount of energy used, and the period of use.
도 3은 동일한 배터리 상태에서도 주행 가능한 거리가 크게 달라지는 모습을 보여주고 있다. 배터리의 상태가 동일하더라도, 주행 되는 코스, 운전자의 운전패턴에 따라 동일 주행거리(km)에 대한 배터리의 에너지 사용량(kwh)이 차이가 나게 된다.Figure 3 shows that even in the same battery state the range that can be driven is significantly different. Even if the state of the battery is the same, the energy consumption (kwh) of the battery for the same driving distance (km) is different according to the driving course, the driving pattern of the driver.
이처럼 배터리의 상태 변화와, 주행 환경에 따른 에너지 소모율의 변화 등은 전기자동차의 정확한 잔존 주행거리 추정에 장애 요인으로 작용한다. 따라서, 종래 기술에 따라 고전압 배터리의 SOC 만을 이용해 잔존 주행거리를 산출하는 것은 그 정확성이 크게 떨어지는 문제점이 있었다.
As such, the change in the state of the battery and the change in the energy consumption rate according to the driving environment are obstacles to the accurate estimation of the remaining mileage of the electric vehicle. Therefore, according to the prior art, calculating the remaining driving distance using only the SOC of the high voltage battery has a problem in that its accuracy is greatly reduced.
따라서 본 발명은 상기와 같은 문제점을 해결하기 위하여 발명한 것으로서, 배터리에 충전되는 에너지와 실제 주행한 거리와의 관계를 학습하여 기존의 잔존 주행거리 추정 방법을 보완함으로 배터리의 상태 변화 및 운전자의 주행 패턴의 차이에도 정확한 잔존 주행거리를 표시할 수 있는 전기자동차 잔존 주행거리 추정방법을 제공하고자 하는데 그 목적이 있다.
Therefore, the present invention has been invented to solve the above problems, by learning the relationship between the energy charged in the battery and the actual distance traveled by complementing the existing method of estimating the remaining distance of the battery state changes and driving of the driver An object of the present invention is to provide a method for estimating the remaining driving distance of an electric vehicle that can display an accurate remaining driving distance even in a difference of patterns.
상기한 목적을 달성하기 위하여 본 발명에 따른 전기자동차 잔존 주행거리 추정방법은, 전기자동차의 주행거리 및 배터리의 충전량을 측정하는 단계; 상기 배터리 충전량에 대한 주행거리를 누적하여 평균한 학습 연산된 배터리 에너지 소모비율을 갱신하는 단계; 배터리의 현재SOC(State of Charge) 및 SOH(State of Health)를 측정하는 단계; 및 상기 측정된 배터리의 현재SOC에 대하여, 학습 연산된 배터리 에너지 소모비율과 SOH를 이용하여 전기자동차의 잔존 주행거리를 추정하는 단계; 를 포함하는 것을 특징으로 한다.In order to achieve the above object, an electric vehicle remaining mileage estimation method according to the present invention comprises the steps of measuring the mileage of the electric vehicle and the amount of charge of the battery; Updating a learning calculated battery energy consumption rate by accumulating and driving the driving distance with respect to the battery charge amount; Measuring a current state of charge (SOC) and a state of health (SOH) of the battery; And estimating the remaining mileage of the electric vehicle using the measured and calculated battery energy consumption ratio and SOH with respect to the measured current SOC of the battery. Characterized in that it comprises a.
이때, 상기 잔존 주행거리를 추정하는 단계에서 잔존 주행거리는
In this case, the remaining driving distance in the step of estimating the remaining driving distance is
본 발명에 따른 전기자동차 잔존 주행거리 추정방법에 의하면, 배터리의 사용환경적 특성, 열화 되는 내부 특성변화 등 다양한 환경에서도 사용자의 특성에 맞게 잔존 주행거리를 추정하므로 에너지 관리에 효율적일 뿐만 아니라, 사용자에게 잔존 주행거리에 대한 정확한 정보를 제공할 수 있다.According to the method for estimating the remaining driving distance of an electric vehicle according to the present invention, the remaining driving distance is estimated according to the user's characteristics even in various environments such as environmental characteristics of the battery and deterioration of internal characteristics of the battery. Accurate information about remaining mileage can be provided.
또한, 본 발명의 실시예에 따르면 배터리의 상태 및 열화 정도를 추정하는 별도의 장치 및 로직 없이도 잔존거리를 정확하게 추정할 수 있게 되어, 추가 비용 없이 상품성을 향상시킬 수 있다.
In addition, according to an embodiment of the present invention it is possible to accurately estimate the remaining distance without a separate device and logic for estimating the state of the battery and the degree of degradation, it is possible to improve the merchandise without additional costs.
도 1은 본 발명의 실시예에 따른 전기자동차 잔존 주행거리 추정방법을 나타내는 순서도.
도 2는 전기자동차 배터리의 성능에 영향을 주는 요소들과 이에 따른 성능 변화를 나타내는 그래프.
도 3은 동일한 배터리 상태에서 주행 환경에 따라 변화하는 주행 가능 거리의 차이를 나타내는 그래프.1 is a flowchart illustrating a method for estimating the remaining driving distance of an electric vehicle according to an exemplary embodiment of the present invention.
2 is a graph showing the factors affecting the performance of the electric vehicle battery and the performance change accordingly.
3 is a graph showing a difference in driving distance that varies depending on the driving environment in the same battery state.
이하, 첨부한 도면을 참조하여 본 발명의 바람직한 실시예를 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 상세히 설명하기로 한다.Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention.
본 발명은 전기자동차의 잔존 주행거리 추정방법에 대한 것으로서, 배터리에 충전되는 에너지와 실제 주행한 거리와의 관계를 학습한 데이터를 축적하고, 이를 통해 현재 SOC 상태에 따른 잔존 주행거리를 추정함으로 잔존 주행거리 추정의 정확도를 높일 수 있는 발명에 대한 것이다. 특히, 본 발명에 따르면 배터리의 상태 변화 및 운전자의 주행 패턴 차이에 따라 차별화된 학습 데이터가 구축되므로 잔존 주행거리 추정의 오차 발생 요인의 영향을 최소화할 수 있다.The present invention relates to a method for estimating the remaining mileage of an electric vehicle, and accumulates data on learning the relationship between the energy charged in the battery and the actual distance traveled, thereby remaining by estimating the remaining mileage according to the current SOC state. The present invention relates to an invention capable of increasing the accuracy of a mileage estimation. In particular, according to the present invention, the differentiated learning data is constructed according to the change in the state of the battery and the difference in the driving pattern of the driver, thereby minimizing the influence of the error occurrence factor of the remaining mileage estimation.
도 1은 본 발명의 실시예에 따른 전기자동차 잔존 주행거리 추정방법을 나타내고 있다.1 illustrates a method for estimating the remaining travel distance of an electric vehicle according to an exemplary embodiment of the present invention.
본 발명에서는, 먼저 차량의 실제 주행한 거리와 고전압 배터리에 충전되는 에너지를 각각 측정하여, 상기 실제 주행 거리에 대한 충전 에너지의 비율을 누적 평균하여 저장할 수 있다.In the present invention, first, the actual driving distance of the vehicle and the energy charged in the high voltage battery are respectively measured, and the ratio of the charging energy to the actual driving distance may be accumulated and stored.
즉, 본 발명의 실시예에 따라 배터리의 이전 충전 후 주행거리(d)를 측정하고(S110), 다음 배터리 충전시의 배터리 충전량(Q)을 측정하여(S112), 배터리 충전량에 대한 주행거리(Q/d)를 산출할 수 있다.That is, according to an exemplary embodiment of the present invention, the driving distance d after the previous charging of the battery is measured (S110), and the battery charging amount Q at the next battery charging is measured (S112). Q / d) can be calculated.
본 발명에서는 배터리 충전시마다 산출되는 배터리 충전량에 대한 주행거리(Q/d) 값을 누적하여 평균한 데이터 즉, 학습 연산된 배터리 에너지 소모비율(B)을 저장할 수 있다. 이때, 상기 학습 연산된 배터리 에너지 소모비율(B)은, S110와 S112 단계를 거치면서 새로 생성된 배터리 충전량에 대한 주행거리(Q/d) 값을 입력받아 누적 평균치를 재산출하고, 재산출된 데이터를 저장하여 갱신할 수 있다.In the present invention, the data obtained by accumulating and averaging the driving distance Q / d value for the battery charge amount calculated at each battery charge, that is, the battery energy consumption ratio B, which is a learning operation, may be stored. In this case, the learning operation battery energy consumption ratio (B) is recalculated, the cumulative average value by inputting the driving distance (Q / d) value for the newly generated battery charge amount through the steps S110 and S112 Data can be saved and updated.
본 발명의 실시예에 따라 배터리 충전이 다시 이루어지게 되면(S120), 상기 S110 내지 S114 단계를 반복할 수 있다. 본 발명에서 학습 연산된 배터리 에너지 소모비율(B)은 학습 초기 단계에 있어서, 이전 충전 후 주행에 따른 배터리 에너지 소모량과 현재 충전되는 배터리 에너지량의 불일치로 인한 오차가 발생할 수 있다. 그러나 배터리 충전량에 대한 주행거리(Q/d) 값의 누적이 이루어짐에 따라 상기 오차는 상쇄될 수 있으며, 차량의 배터리 상태 및 운전자의 운전 습관에 따른 영향이 반영된 학습 데이터(B)를 구비할 수 있게 된다.When the battery is recharged according to an embodiment of the present invention (S120), the steps S110 to S114 may be repeated. In the learning operation, the battery energy consumption rate B may be an error due to a mismatch between the battery energy consumption according to the driving after the previous charging and the amount of battery energy currently charged. However, as the accumulation of the driving distance (Q / d) value for the battery charge is accumulated, the error may be offset, and the learning data B reflecting the influence of the battery state of the vehicle and the driving habit of the driver may be provided. Will be.
한편, 운전자가 전기자동차의 잔존 주행거리 정보를 요청하게 되면(S130), 본 발명의 실시예에 따라 전기자동차의 BMS(Battery Management System)에서는 배터리의 현재 SOC(State of Charge)와 SOH(State of Health)를 측정할 수 있다(S132). 여기서 상기 SOC 및 SOH를 측정하는 방법은 공지된 기술을 사용할 수 있으며, 이에 대한 자세한 설명은 생략한다.On the other hand, when the driver requests the remaining mileage information of the electric vehicle (S130), according to an embodiment of the present invention in the battery management system (BMS) of the electric vehicle according to the present state of charge (SOC) and state of charge (SOH) of the battery Health) can be measured (S132). Here, the method for measuring the SOC and SOH may use a known technique, a detailed description thereof will be omitted.
본 발명에서는 상기 측정된 배터리의 현재 SOC에 대한 잔존 주행거리를 추정할 수 있다. 이때, 학습 연산된 배터리 에너지 소모비율(B)과 배터리의 SOH를 이용하여 다음과 같은 수식으로 잔존 주행거리를 추정할 수 있다(S134).In the present invention, it is possible to estimate the remaining travel distance with respect to the current SOC of the measured battery. In this case, the residual driving distance may be estimated using the following formula, using the learning operation battery energy consumption ratio B and the SOH of the battery (S134).
여기서 α 및 β는 연산가중치비율로서 β=1-α의 관계를 가질 수 있으며, A는 배터리 에너지 소모비율(km/SOC), B는 본 발명의 실시예에 따라 산출된 학습 연산된 배터리 에너지 소모비율(km/SOC·kwh)을 나타낸다.Here, α and β may have a relationship of β = 1-α as an operation weight ratio, A is a battery energy consumption ratio (km / SOC), and B is a learning operation battery energy consumption calculated according to an embodiment of the present invention. The ratio (km / SOC · kwh) is shown.
수학식 1에 나타난 바와 같이, 본 발명에서는 단순히 현재 SOC에 에너지 소모비율(A)을 곱한 잔존 주행거리 추정값을 보완하여, 학습 연산된 배터리 에너지 소모비율(B)을 이용한 보정을 수행한다.As shown in Equation 1, the present invention simply compensates the remaining mileage estimate obtained by multiplying the current SOC by the energy consumption ratio A, and performs correction using the learned and calculated battery energy consumption ratio B.
구체적으로, 상기 보정값은 현재 SOC값에 학습 연산된 배터리 에너지 소모비율(B), 배터리의 공칭에너지(kwh) 및 배터리 SOH를 곱한 값이 될 수 있다. 상기 배터리의 공칭에너지는 배터리의 규격화된 정격 용량을 나타내는 것으로서 백분율로 나타나는 SOH를 곱함으로 실제 사용가능한 최대 용량을 나타낼 수 있다. 이때, 현재 SOC값을 고려하여 배터리의 현재 잔존 에너지를 구할 수 있고, 본 발명의 실시예에 따른 학습 연산된 배터리 에너지 소모비율(B)과의 관계를 통해 잔존거리 추정의 보정값을 산출할 수 있게 된다.Specifically, the correction value may be a value obtained by multiplying the current SOC value by the battery energy consumption ratio B, the nominal energy kwh of the battery, and the battery SOH. The nominal energy of the battery represents the normalized rated capacity of the battery and can be represented by the maximum available capacity by multiplying the SOH expressed as a percentage. In this case, the current remaining energy of the battery may be obtained in consideration of the current SOC value, and a correction value of the remaining distance estimation may be calculated through a relationship with the learned and calculated battery energy consumption ratio B according to an embodiment of the present invention. Will be.
본 발명의 바람직한 실시예에 따르면, 상기 에너지 소모비율(A)을 이용하여 산출한 잔존 주행거리 추정값과, 학습 연산된 배터리 에너지 소모비율(B)을 이용하여 산출한 보정값을 적절한 연산가중치(α, β)를 적용하여 합산할 수 있다. 이때, 상기 연산가중치(α, β)는 0에서 1 사이의 값을 가질 수 있으며, 전기 자동차의 주행환경 또는 상태에 따라 적절히 변경 가능하다.According to a preferred embodiment of the present invention, an appropriate calculation weight value α may be calculated by using the estimated residual distance calculated using the energy consumption ratio A and the correction value calculated using the learned battery energy consumption ratio B. , β) can be applied to add up. In this case, the calculation weight values α and β may have a value between 0 and 1, and may be appropriately changed according to the driving environment or state of the electric vehicle.
이처럼 본 발명에서 제공하는 전기자동차 잔존 주행거리 추정방법은 실제 주행에 따른 학습 데이터를 사용함으로 배터리의 사용환경적 특성, 열화 되는 내부 특성 변화 등의 다양한 환경에서도 사용자의 특성에 맞게 잔존 주행거리를 추정할 수 있다. 특히, 본 발명의 실시예에 따른 잔존 주행거리 추정방법은 배터리의 상태나 열화 정도를 추정하는 별도의 장치가 필요하지 않으며, 기존의 BMS 상에 구비되는 입력값 및 측정값을 통해 간단하고 정확하게 잔존 주행거리를 추정할 수 있게 된다.As described above, the method for estimating the remaining driving distance of the electric vehicle provided by the present invention estimates the remaining driving distance according to the user's characteristics even in various environments such as environmental characteristics of the battery and deterioration of internal characteristics by using learning data according to actual driving. can do. In particular, the method of estimating the remaining mileage according to the embodiment of the present invention does not need a separate device for estimating the state of the battery or the degree of deterioration, and simply and accurately remains through the input values and the measured values provided on the existing BMS. The mileage can be estimated.
이상에서는 본 발명을 구체적인 실시예를 통하여 설명하였으나, 당업자라면 본 발명의 취지 및 범위를 벗어나지 않고 수정, 변경을 할 수 있다. 따라서 본 발명의 상세한 설명 및 실시예로부터 본 발명이 속하는 기술분야에 속한 사람이 용이하게 유추할 수 있는 것은 본 발명의 권리범위에 속하는 것으로 해석된다.
In the above described the present invention through specific embodiments, those skilled in the art can make modifications, changes without departing from the spirit and scope of the present invention. Therefore, what can be easily inferred by the person of the technical field to which this invention belongs from the detailed description and the Example of this invention is interpreted as belonging to the scope of the present invention.
Claims (2)
상기 배터리 충전량에 대한 주행거리를 누적하여 평균한 학습 연산된 배터리 에너지 소모비율을 갱신하는 단계;
배터리의 현재SOC(State of Charge) 및 SOH(State of Health)를 측정하는 단계; 및
상기 측정된 배터리의 현재SOC에 대하여, 학습 연산된 배터리 에너지 소모비율과 SOH를 이용하여 전기자동차의 잔존 주행거리를 추정하는 단계;
를 포함하는 것을 특징으로 하는 전기자동차 잔존 주행거리 추정방법.
Measuring the mileage of the electric vehicle and the amount of charge of the battery;
Updating a learning calculated battery energy consumption rate by accumulating and driving the driving distance with respect to the battery charge amount;
Measuring a current state of charge (SOC) and a state of health (SOH) of the battery; And
Estimating the remaining mileage of the electric vehicle using the measured and calculated battery energy consumption ratio and SOH with respect to the measured current SOC of the battery;
Electric vehicle residual mileage estimation method comprising a.
상기 잔존 주행거리를 추정하는 단계에서 잔존 주행거리는
로 산출하는 것을 특징으로 하는 전기자동차 잔존 주행거리 추정방법.
α : 연산가중치비율
β : 연산가중치비율(1-α)
A : 배터리 에너지 소모비율
B : 학습 연산된 배터리 에너지 소모비율The method of claim 1,
In the step of estimating the remaining driving distance is the remaining driving distance
Residual mileage estimation method for an electric vehicle, characterized in that calculated by.
α: operation weight ratio
β: operation weight ratio (1-α)
A: Battery energy consumption rate
B: Learned Battery Energy Consumption Rate
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