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KR100896216B1 - Battery prediction control algorism for hybrid electric vehicle - Google Patents

Battery prediction control algorism for hybrid electric vehicle Download PDF

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KR100896216B1
KR100896216B1 KR1020070112402A KR20070112402A KR100896216B1 KR 100896216 B1 KR100896216 B1 KR 100896216B1 KR 1020070112402 A KR1020070112402 A KR 1020070112402A KR 20070112402 A KR20070112402 A KR 20070112402A KR 100896216 B1 KR100896216 B1 KR 100896216B1
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hybrid electric
battery
charge rate
battery charge
electric vehicle
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정연종
서범주
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/13—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/04—Traffic conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Hybrid Electric Vehicles (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

본 발명에서는 도 3과 같이 하이브리드 전기자동차의 목적지 구배패턴, 주행패턴, 운전패턴 등을 결정하기 위하여, 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)의 도로정보, 교통정보, 가속, 감속, 제동, 조향, 하중 등의 정보를 이용하여 가중 배터리 충전율인 SOCweight(280)를 결정하고, 예측 배터리 충전율인 SOCpred(120)를 결정하여 도 2와 같이 하이브리드 전기자동차의 배터리 SOC에 사용함으로써, 효과적인 전기 에너지의 생산, 저장 및 배분을 통하여 하이브리드 전기자동차의 에너지 효율을 높이고, 하이브리드 전기자동차에 탑재된 엔진이나 연료전지의 작동을 최대효율 운전범위로 최대한 확대함으로써, 에너지 소비효율을 개선하고, 배터리의 과방전 과충전을 줄임으로서 배터리의 수명을 증가시키는 것이 특징인 하이브리드 전기자동차용 배터리의 예측제어방법이다.In the present invention, in order to determine the destination gradient pattern, driving pattern, driving pattern, etc. of the hybrid electric vehicle as shown in Figure 3, the road information, traffic information, acceleration, By determining the SOCweight 280 which is the weighted battery charge rate using information such as deceleration, braking, steering, and load, and determining the SOCpred 120 which is the predicted battery charge rate and using it in the battery SOC of the hybrid electric vehicle as shown in FIG. Improve energy efficiency of hybrid electric vehicles by effectively producing, storing and distributing electric energy, and improving the energy consumption efficiency by extending the operation of engines and fuel cells mounted on hybrid electric vehicles to the maximum efficiency operating range. Hybrid electric vehicle, characterized by increasing battery life by reducing over discharge and over charging Predictive control method of battery.

하이브리드 전기자동차, GPS, Navigation, 위치정보시스템, 전자지도, 도로정보, 배터리 충전율, SOC Hybrid Electric Vehicle, GPS, Navigation, Geolocation System, Electronic Map, Road Information, Battery Charge Rate, SOC

Description

하이브리드 전기자동차용 배터리의 예측제어방법{Battery prediction control algorism for Hybrid Electric Vehicle}Battery predictive control method for hybrid electric vehicle {Battery prediction control algorism for Hybrid Electric Vehicle}

하이브리드 전기자동차는 도 1과 같이 구동 에너지원이 엔진(104), 외부 충전장치(113), 연료전지(109), 엔진과 외부충전장치(101,113), 엔진과 연료전지(104,109), 외부충전장치와 연료전지(109,113), 엔진과 외부충전장치와 연료전지(104,113,109) 등의 조합으로 구성되며, 바퀴의 구동력은 엔진(104)과 모터(107), 모터(107)의 조합으로 구성된다. 그리고 하이브리드 전기자동차는 전기 모터를 구동하기 위하여 전기 에너지 저장장치(106)가 필요하다. 전기 에너지 저장 장치는 재충전이 가능해야 하며, 배터리, 울트라캡(Ultra Capacitor), 배터리와 울트라캡의 조합 등을 포함하고 있다. 전기에너지를 배터리에 충전하고 회생제동 에너지를 얻기 위하여 전기모터(105)는 발전기(107)의 기능이 있으며, 추가로 별도의 전기 발전기(107)를 장착한 하이브리드 전기자동차도 있다. In the hybrid electric vehicle, as shown in FIG. 1, the driving energy source is the engine 104, the external charging device 113, the fuel cell 109, the engine and the external charging device 101, 113, the engine and the fuel cell 104, 109, the external charging device. And a combination of the fuel cells 109 and 113, the engine and the external charging device, and the fuel cells 104, 113 and 109, and the driving force of the wheel is composed of the combination of the engine 104, the motor 107, and the motor 107. In addition, the hybrid electric vehicle requires an electric energy storage device 106 to drive an electric motor. The electrical energy storage device must be rechargeable and includes a battery, ultracapacitor, a combination of battery and ultracap. The electric motor 105 has a function of the generator 107 in order to charge the electric energy to the battery and obtain regenerative braking energy, and there is also a hybrid electric vehicle equipped with a separate electric generator 107.

이와 같은 하이브리드 전기자동차에서 배터리 충전율을 SOC(State of Charge)라고 하는데, 배터리 SOC 설정값 및 충방전 알고리즘이 하이브리드 전기자동차의 에너지 소비효율 및 배터리의 수명에 직접적인 영향을 미치게 된다. 일반적으로 하이브리드 전기자동차는 배터리의 SOC를 가능한 일정하게 유지하면서 전기 에너지를 많이 사용하는 것이 배터리의 수명을 연장하고 하이브리드 전기자동차의 에너지 소비효율을 개선하는데 유리한 것으로 되어 있다. In such a hybrid electric vehicle, the battery charge rate is called a state of charge (SOC). The battery SOC setting value and the charge / discharge algorithm directly affect the energy consumption efficiency and the battery life of the hybrid electric vehicle. In general, the hybrid electric vehicle is to use a lot of electric energy while keeping the SOC of the battery as constant as possible, it is advantageous to extend the life of the battery and improve the energy consumption efficiency of the hybrid electric vehicle.

하이브리드 전기자동차에서 배터리 충전율을 SOC(State of Charge)라고 하는데, 배터리 SOC 설정값 및 충방전 알고리즘이 하이브리드 전기자동차의 에너지 소비효율 및 배터리의 수명에 직접적인 영향을 미치게 된다. 일반적으로 하이브리드 전기자동차는 배터리의 SOC를 가능한 일정하게 유지하면서 전기에너지를 많이 사용하는 것이 배터리의 수명을 연장하고 하이브리드 전기자동차의 에너지 소비효율을 개선하는데 유리한 것으로 되어 있다. 그러나 하이브리드 전기자동차의 주행 조건이 산악지형이나 평지 등 도로구배에 따라 다르고, 고속도로, 일반도로, 도심 등 주행속도 및 가속 정지 등 주행패턴이 다르며, 운전자의 운전습관에 따라 가속, 감속, 제동, 조향 등 운전자에 따른 운전패턴의 정도가 달라 모든 조건을 만족하는 최적의 배터리 SOC를 결정하기가 매우 어렵다. 이상과 같은 이유 때문에 모든 조건을 만족시키는 배터리 SOC를 결정하기 위하여 일반적으로 높은 수치의 배터리 SOC를 기본 배터리 충전율인 SOCbas(101)로 결정하여 사용한다. 기본 배터리 충전율인 SOCbas(101)를 높게 설정하면 산악지형의 등판에는 유리하나, 내리막길에서 효과적으로 제동회생에너지의 회수가 어렵고, 기본 배터리 충전율인 SOCbas를 낮게 설정하면 산악지형의 등판이나 과격운전 과정에서 배터리의 방전으로 인해 최악의 경우에 하이브리드 전기자동차가 구동 불능상태가 되거나, 저속운전과정을 통해 배터리를 충전해야 하는 경우가 발생할 수 있다. In a hybrid electric vehicle, the battery charge rate is called a state of charge (SOC). The battery SOC setting value and the charge / discharge algorithm directly affect the energy consumption efficiency and the battery life of the hybrid electric vehicle. In general, hybrid electric vehicles use electric energy while maintaining the SOC of the battery as constant as possible, which is advantageous in extending the life of the battery and improving the energy consumption efficiency of the hybrid electric vehicle. However, the driving conditions of hybrid electric vehicles vary according to road slopes such as mountainous terrain or flat land, and the driving patterns such as highway speed, general roads, urban areas, etc., and driving patterns such as acceleration and deceleration stop, and acceleration, deceleration, braking, steering depending on driver's driving habits. It is very difficult to determine the optimal battery SOC that satisfies all conditions because the degree of driving pattern varies depending on the driver. For the above reasons, in order to determine a battery SOC that satisfies all conditions, a high value of the battery SOC is generally used as the SOCbas 101 which is a basic battery charge rate. Setting the SOCbas (101), which is the basic battery charge rate, is high, it is advantageous for mountain climbing, but it is difficult to recover the braking energy on the downhill, and setting the SOCbas, which is the basic battery charge rate, is low during the mountaineering or extreme driving process. Due to the discharge of the battery, in the worst case, the hybrid electric vehicle may become inoperable or the battery may need to be charged through a low speed driving process.

하이브리드 전기자동차에서 배터리 충전율을 SOC(State of Charge)라고 하는데, 배터리 SOC의 설정값은 하이브리드 전기자동차의 에너지 소비효율 및 배터리의 수명에 직접적인 영향을 미치게 된다. 그러나 하이브리드 전기자동차의 주행 조건이 산악지형이나 평지 등 도로구배에 따라 다르고, 고속도로, 일반도로, 도심 등 주행속도 및 가속 정지 등 주행패턴이 다르며, 운전자의 운전패턴에 따라 가속, 감속, 제동, 조향의 정도가 달라 모든 주행조건을 만족하는 최적의 배터리 SOC를 결정할 수 없다. 기본 배터리 충전율인 SOCbas(101) 높게 설정하면 산악지형의 등판에는 유리하나, 내리막길에서 효과적으로 제동회생에너지의 회수가 어렵고, 기본 배터리 충전율인 SOCbas를 낮게 설정하면 산악지형의 등판이나 과격운전 과정에서 배터리의 방전으로 인해 최악의 경우에 하이브리드 전기자동차가 구동 불능상태가 되거나, 저속운전과정을 통해 배터리를 충전해야 하는 경우가 발생할 수 있다. 기존에는 이와 같은 하이브리드 자동차의 구동불능 과 저속 운전 등의 문제를 사전에 방지하기 위하여 필요 이상의 고출력 엔진이나 연료전지를 사용하거나, 운전 효율이 나쁜 저출력, 고출력 부분에서 운전되는 경우가 많아지고, 필요 이상의 높은 배터리 SOCbas(101)를 유지하는 방법을 사용하였다. 이와 같은 결과 하이브리드 전기자동차의 에너지 소비효율이 저하되고, 배터리의 수명이 단축되는 결과를 가져 왔다. In a hybrid electric vehicle, the battery charge rate is called a state of charge (SOC). The setting value of the battery SOC has a direct influence on the energy consumption efficiency and battery life of the hybrid electric vehicle. However, the driving conditions of hybrid electric vehicles vary according to the road slope such as mountainous terrain or flat land, and the driving patterns such as the speed and acceleration stop of highways, general roads, and urban areas are different.Acceleration, deceleration, braking, steering The degree of accuracy is different and the optimum battery SOC that satisfies all driving conditions cannot be determined. SOCbas (101), which is the basic battery charge rate, is high, which is advantageous for mountainous terrains, but it is difficult to recover braking energy on downhill roads. In the worst case, a hybrid electric vehicle may become inoperable or a battery may need to be charged through a low speed operation due to the discharge of. Conventionally, in order to prevent such problems such as drive failure and low speed operation of a hybrid vehicle in advance, a high-power engine or a fuel cell more than necessary is often used, or a low-power or high-power part with poor driving efficiency is often operated. A method of maintaining high battery SOCbas 101 was used. As a result, the energy consumption efficiency of the hybrid electric vehicle is reduced and the battery life is shortened.

본 발명은 도 3과 같이 하이브리드 전기자동차가 주행하게 될 목적지까지 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)의 도로정보, 교통정보, 가속, 감속, 제동, 조향, 하중 등의 정보를 이용하여 목적 지점까지 도로 구배패턴, 주행패턴, 운전자의 운전 패턴 등을 고려하여 주행조건에 적합한 가중 배터리 충전율인 SOCweight(280)를 결정하고, 도 2와 같이 실시간으로 예측 배터리 충전율인 SOCpred(120)를 사용하는 것을 특징으로 하는 하이브리드 전기자동차용 배터리의 예측 제어방법이다. According to the present invention, road information, traffic information, acceleration, deceleration, braking, steering, load, etc. of a digital map supported by a location information system (GPS) to a destination where the hybrid electric vehicle will drive as shown in FIG. 3. Determination of the SOCweight 280, which is a weighted battery charging rate suitable for driving conditions, in consideration of a road gradient pattern, a driving pattern, a driver's driving pattern, and the like to the destination point, and SOCpred 120 which is a predicted battery charging rate in real time as shown in FIG. Predictive control method for a hybrid electric vehicle battery, characterized in that the use of ().

예를 들어, 운행 중인 하이브리드 전기자동차는 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)를 이용하여 목적지까지 주행하게 될 도로를 사전에 설정하고, 주행하게 될 도로의 구배패턴 및 교통정보를 기반으로 최적의 주행 에너지 분배를 예측하여 GPS 배터리 충전율인 SOCgps(210)를 결정한다.       For example, a hybrid electric vehicle in operation sets a road to be driven to a destination in advance by using a digital map supported by a location information system (GPS), and a gradient pattern and traffic information of the road to be driven. The SOCgps 210, which is a GPS battery charge rate, is determined by predicting an optimal driving energy distribution based on the

운행 중인 하이브리드 전기자동차는 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)를 이용하여 목적지까지 주행하게 될 도로를 고속도로, 일반도로, 도심주행 등으로 구분하여 평균 주행속도 및 주행속도의 변동률을 기반으로 예측되는 주행패턴을 분석하여 주행패턴 배터리 충전율인 SOCspeed(230)를 결정한다.     The hybrid electric vehicle in operation uses the digital map supported by the location information system (GPS) to classify the road to the destination into highways, general roads, and city driving, and thus changes in the average driving speed and driving speed. The driving pattern predicted based on the analysis of the driving pattern battery charge rate SOCspeed 230 is determined.

운행 중인 하이브리드 전기자동차는 운전자의 가속, 감속, 제동, 조향의 정도를 고려한 운전패턴을 분석하여 운전패턴 배터리 충전율인 SOCdriver(220)를 결정한다.     The hybrid electric vehicle in operation determines the SOCdriver 220 which is the driving pattern battery charge rate by analyzing the driving pattern in consideration of the degree of acceleration, deceleration, braking and steering of the driver.

이상과 같은 GPS 배터리 충전율인 SOCgps와 주행패턴 배터리 충전율인 SOCspeed, 운전패턴 배터리 충전율인 SOCdriver를 기반으로 하며, 도 3과 같이 가 중 배터리 충전율인 SOCweight(280)를 결정하게 되고, 하이브리드 전기자동차의 기본 SOCbas(101)를 도 2와 같이 예측 배터리 충전율인 SOCpred(120)로 변경하여 사용한다.     Based on the above-mentioned GPS battery charge rate SOCgps, driving pattern battery charge rate SOCspeed, driving pattern battery charge rate SOCdriver, as shown in Figure 3 is to determine the weighted battery charge rate SOCweight (280), the basis of the hybrid electric vehicle The SOCbas 101 is changed to the SOCpred 120 which is a predicted battery charge rate as shown in FIG. 2.

예측 배터리 충전율인 SOCpred를 구하는 함수식은 다음과 같다.     The function formula for SOCpred, which is a predicted battery charge rate, is as follows.

도 2(120)에서 SOCpred=f(SOCbas, SOCweight)2, SOCpred = f (SOCbas, SOCweight)

가중 배터리 충전율인 SOCweight를 구하는 함수식은 다음과 같다.     The function formula for SOCweight, which is a weighted battery charge rate, is as follows.

도 3(280)에서 SOCweight=f(SOCgps, SOCspeed, SOCdriver)SOCweight = f (SOCgps, SOCspeed, SOCdriver) in FIG. 3 (280)

예측 배터리 충전율인 SOCpred(120)를 구하는 함수식과 가중 배터리 충전율인 SOCweight(250)를 구하는 함수식은 수학연산, 논리연산, 회귀연산, 퍼지연산, 신경망 연산 방법 등 다양한 알고리즘을 사용할 수가 있다.     The functional formula for obtaining SOCpred 120, which is the predicted battery charge rate, and the SOCweight 250, for the weighted battery charge rate, may use various algorithms such as mathematical operation, logical operation, regression operation, fuzzy operation, and neural network operation method.

본 발명에서는 도 3과 같이 목적지의 구배패턴, 주행패턴, 운전패턴 등을 결정하기 위하여, 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)의 도로정보, 교통정보, 가속, 감속, 제동, 조향, 하중 등의 정보를 이용하여 가중 충전율인 SOCweight(280)를 결정하고, 예측충전율인 SOCpred(120)를 결정하여 도 3과 같이 하이브리드 전기자동차의 배터리 SOC에 사용함으로써, 효과적인 전기 에너지의 생산, 저장 및 배분을 통하여 하이브리드 전기자동차의 에너지 효율을 높이고, 하이브리드 전기자동차에 탑재된 엔진이나 연료전지의 작동을 최대효율 운전범위로 최대한 확대함으로써, 에너지 소비효율을 개선하고, 배터리의 과방전 과충전을 줄임으로써 배터리의 수명을 증가시키는 효과가 있다.In the present invention, to determine the gradient pattern, driving pattern, driving pattern, etc. of the destination as shown in Figure 3, the road information, traffic information, acceleration, deceleration, braking of the digital map (GPS) supported by the location information system (GPS) The SOCweight 280, which is a weighted charge rate, is determined using information on steering, load, and the like, and the SOCpred 120, which is a predicted charge rate, is determined and used in a battery SOC of a hybrid electric vehicle as shown in FIG. The energy efficiency of hybrid electric vehicles is increased through storage, distribution and distribution, and the operation of engines and fuel cells mounted on hybrid electric vehicles is maximized to the maximum efficiency operating range, thereby improving energy consumption efficiency and over-discharging overcharging of batteries. Reducing has the effect of increasing the life of the battery.

본 발명은 하이브리드 전기자동차에서 도 2와 같이 구성방법에 따라 직렬 하이브리드 전기자동차(111), 병렬 하이브리드 전기자동차(111, 112), 동력분기식 하이브리드 전기자동차(111,112), 플러그인 직렬하이브리드 전기자동차(111,113), 플러그인 병렬하이브리드 전기자동차(111,112,113), 연료전지 전기자동차(119), 플러그인 연료전지 하이브리드 전기자동차(113, 119), 연료전지 직렬 하이브리드 전기자동차(111, 119), 연료전지 병렬 하이브리드 전기자동차(111,112,119) 등으로 구분이 되는, 하이브리드 전기자동차에 도 3의 SOCweight(280)를 계산하여 도 2와 같이 하이브리드 전기자동차에 SOCpred(120)를 사용하는 배터리 SOC 제어방법을 적용하여 아래와 같은 특징이 있는 하이브리드 전기자동차용 배터리의 예측제어방법에 관한 발명이다. The present invention is a hybrid electric vehicle in series hybrid electric vehicle 111, parallel hybrid electric vehicle 111, 112, power divergent hybrid electric vehicle (111, 112), plug-in series hybrid electric vehicle (111, 113) according to the configuration method as shown in FIG. , Plug-in parallel hybrid electric vehicle (111, 112, 113), fuel cell electric vehicle (119), plug-in fuel cell hybrid electric vehicle (113, 119), fuel cell series hybrid electric vehicle (111, 119), fuel cell parallel hybrid electric vehicle ( The hybrid SOC control method using the SOCpred 120 using the SOCpred 120 in the hybrid electric vehicle is calculated by calculating the SOCweight 280 of FIG. The present invention relates to a predictive control method for an electric vehicle battery.

한가지의 실시한 예로 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)에서 사용하게 될 경로 인자는 도 4와 같은 주행하게 될 도로의 수직 구배(301,302,303,304,305,306,307,308,309,310,311,312)이다. 즉 하이브리드 전기자동차는 현재 시점을 기준으로 하여 1분 후부터 60분 동안 주행하게 될 도로의 평지주행(도로 구배율=0%) 오르막주행(도로 구배율>0%) 내리막주행(도로구배율<0%)을 기초로 하여 GPS 배터리 충전율인 SOCgps(210)를 결정하게 된다. 그 방법은 도 5와 같은 알고리즘으로, 하이브리드 전기자동차가 현재는 평지주행(301)을 하고 있는데 앞으로 5분 후에 5분 동안 도로 구배율>0%(211)인 구간(303)을 운행하게 된다는 것을 미리 예측(214)하고 미리 평지구간(302)에서 GPS 배터리 충전율인 SOCgps(502)을 최적의 엔진 운전효율이나 최적의 연료전지 운전효율로 미리 충전하게 된다. 주행 개시 15분 후에는 다시 내리막주행(304)을 해서 제동회생 에너지의 량을 예측(217) 계산할 수 있으므로, 예측 결과 출발 후 15분이 될 때에 제동 회생에너지를 회수하기 위하여 배터리의 전기에너지를 최대한 소비하여 SOCpred(503)를 최소로 유지한다. 다시 출발 후 15분 후부터 5분 동안의 내리막 주행(304) 및 5분 동안의 평지주행(305) 후에는 다시 오르막 주행(306, 308)을 하는 것을 GPS를 이용한 디지털 지도의 정보를 통하여 예측(211, 212)할 수 있으므로 주행 시작 후 15분 후부터 25분까지는 오르막 주행(306, 308)을 위하여 SOCgps(504, 505)를 최대한 높임으로서 25분 후부터 40분까지 평지를 포함한 오르막 주행(306, 308)을 무리 없이 주행할 수 있다. 40분까지 오르막 주행 후에는 20분 동안 내리막 주행이 있을 것을 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)에서 예측(213)할 수 있으므로 40분에 오르막 끝에 도착할 때까지 배터리의 전기에너지를 최대한 소비하여 SOCgps(508)를 최소한으로 유지하면 다음 20분 동안 내리막에서 최대로 제동 회생에너지를 회수하여 SOCgps(509,510)을 최대로 증가시켜, 다음에 지속되는 평지에서는 회수된 제동회생 전기에너지를 평지주행에 알맞게 사용함으로써 최종적으로 하이브리드 전기자동차의 에너지 소비 충전 및 분배를 최대 효율적으로 하게 되고 결과적으로 하이브리드 전기자동차의 에너지 소비효율의 상승 및 배터리의 수명증가를 가져올 수 있다. 본 발명의 실시한 예에서 지칭하는 주행시간 및 배터리 SOC 값 등은 하이브리드 전기자동차의 차종, 하중, 도로구배패턴, 주행패턴, 운전패턴, 엔진의 성능, 발전기의 성능, 모터의 성능, 연료전지의 성능, 배터리의 성능 및 각 부품의 노후 정도에 따라 변경될 수 있다.     As an example, the path factor to be used in the digital map supported by the location information system (GPS) is a vertical gradient (301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, and 312) of the road to be driven as shown in FIG. In other words, hybrid electric vehicles drive on flat ground (road slope ratio = 0%) uphill (road slope ratio> 0%) downhill (road slope ratio <0) %) To determine the GPS battery charge rate SOCgps 210. The method is an algorithm such as that of FIG. 5, wherein a hybrid electric vehicle is currently driving on a flat driving 301, and will operate a section 303 having a road gradient> 0% 211 for 5 minutes in the next five minutes. The prediction 214 is performed in advance, and the SOCgps 502 which is the GPS battery charging rate in advance in the flat section 302 is precharged with the optimum engine operating efficiency or the optimum fuel cell operating efficiency. After 15 minutes from the start of driving, the downhill driving 304 can be performed again to calculate the amount of braking regenerative energy (217). Therefore, the electric energy of the battery is consumed as much as possible to recover the braking regenerative energy at 15 minutes after the start of the prediction. SOCpred 503 is kept to a minimum. 15 minutes after the departure, the downhill driving 304 for 5 minutes and the flat driving 305 for 5 minutes, then the uphill driving 306, 308 is predicted through the information of the digital map using GPS (211) Uphill driving (306, 308) from 25 minutes to 40 minutes by increasing the SOCgps (504, 505) as much as possible for the uphill driving (306, 308) from 15 minutes to 25 minutes after the start of driving. You can drive without difficulty. After driving uphill to 40 minutes, you can predict 213 downhill driving for 20 minutes on a digital map supported by the GPS system, so the electrical energy of the battery until you reach the end of uphill at 40 minutes Maximal consumption of SOCgps (508) recovers the maximum braking regenerative energy from the downhill for the next 20 minutes, increasing the SOCgps (509,510) to the maximum, and then recovers the braking regenerative electrical energy recovered on subsequent flats. When used properly for flat driving, the energy consumption charging and distribution of the hybrid electric vehicle can be finally maximized, resulting in an increase in the energy consumption efficiency of the hybrid electric vehicle and an increase in battery life. The driving time and the battery SOC value referred to in the exemplary embodiment of the present invention include the vehicle model, load, road gradient pattern, driving pattern, driving pattern, engine performance, generator performance, motor performance, and fuel cell performance of the hybrid electric vehicle. As a result, the performance of the battery and the degree of deterioration of each component may vary.

또 다른 실시한 예로, 운행 중인 하이브리드 전기자동차는 도 6과 같이 도로 종류에 따라, 평균 주행속도(321,322,323,324,325,326) 및 주행속도의 변동률을 기반으로 주행패턴 배터리 충전율인 SOCspeed(230)를 결정한다. 운행 중인 하이브리드 전기자동차에서 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)에서 사용하게 될 경로 인자는 주행하게 될 도로의 법정 허용속도 및 도로의 교차로 등을 예측해서 주행패턴 배터리 충전율인 SOCspeed(230)를 결정하게 되는데, 고속도로(321,322)를 주행하게 되면 일반적으로 급가속 및 제동의 횟수가 감소하고 정속 운행을 하게 되는데, 이때에는 엔진의 최대 운전효율 이나 연료전지의 최대 운전효율과 배터리의 최대 효율 에너지 배분점에서 운행을 하게 되고, 고속도로 운행을 끝내고 일반도로로 나오기 전(322)에 미리 일반도로의 허용 속도(323) 및 도로의 굴곡을 예측하여 주행패턴 배터리충전율인 SOCspeed(602)를 미리 조절함으로써 지속적으로 엔진이 최대 효율점으로 운전하게 되는 것이다. 또한, 하이브리드 전기자동차가 일반도로(323,324)를 지나서 도심도로(325,326)를 통과하도록 경로가 설정되어 있으면, 이 구간에서는 교차로에서 정지 후 출발과정이 증가하고, 방향 전환에서 감속 및 가속 횟수가 증가하므로 모터의 기동 및 제동회생의 횟수가 증가하여 결국, 배터리 전기에너지의 소모 및 충전횟수가 많아짐으로, 미리 도심주행에 알맞은 주행속도 배터리충전율인 SOCspeed(604)로 미리 예측 조절하여 엔진과 연료전지의 최대 운전효율 범위를 증가시키고 배터리의 수명을 증가시킬 수 있다.     As another example, the hybrid electric vehicle in operation determines SOCspeed 230, which is the driving pattern battery charging rate, based on the average driving speeds 321, 322, 323, 324, 325, 326 and the rate of change of the driving speed according to the road type as shown in FIG. 6. The path factor to be used in the digital map supported by the GPS system in the hybrid electric vehicle in operation is SOCspeed, which is the charging pattern of the driving pattern battery by predicting the legal allowable speed of the road to be driven and the intersection of the road. 230, the driving of the highways 321 and 322 generally reduces the number of rapid accelerations and brakes and operates at a constant speed. In this case, the maximum operating efficiency of the engine or the maximum operating efficiency of the fuel cell and the battery It operates at the maximum efficiency energy distribution point and predicts the allowable speed 323 of the general road and the curvature of the road before exiting the highway and exits the general road (322). By adjusting, the engine continues to run at its maximum efficiency point. In addition, if the hybrid electric vehicle is set to pass through the general roads (323, 324) and through the city roads (325, 326), the departure process after stopping at the intersection increases in this section, and the number of decelerations and accelerations in the direction change increases. As the number of starting and braking regenerative motors increases, the consumption and recharging frequency of battery electric energy increases, so that the engine and fuel cell can be maximized by predicting and adjusting in advance with SOCspeed (604), which is a driving speed battery charging rate suitable for urban driving. It can increase the operating efficiency range and increase the battery life.

또 다른 실시한 예로, 운행 중인 하이브리드 전기자동차는 운전자의 가속, 감 속, 제동, 조향 등의 운전패턴을 분석하여 도 7과 같이 운전패턴 배터리 충전율인 SOCdriver(220)를 결정한다. 이것은 운전자의 운전 패턴을 분석하여 각각의 운전자의 운전 습관에 최적화된 운전자 배터리 충전율인 SOCdriver(220)를 결정하는 것으로서, 기본적으로는 SOCdriver(703)를 기본으로 설정하여 놓고, 급가속 급제동 등이 많은 하이브리드 전기자동차는 단계적으로 SOCdriver(702,701)를 높게 설정해야 운전자의 요구를 충족시킬 수 있을 것이며, 완만한 가속, 완만한 제동을 선호하는 운전자는 운전자 배터리 충전율을 단계적으로 SOCdriver(704,705)를 약간 낮게 설정해도 무리가 없이 효율적으로 하이브리드 전기자동차의 운행이 가능할 것으로 판단된다. 이것은 자동으로 알고리즘을 통해 설정이 될 수도 있으며, Power 1, Power 2, Normal, Econo 1, Econo 2 와 같이 매뉴얼 스위치로 설정될 수도 있다.     As another example, the hybrid electric vehicle in operation determines a driving pattern battery charge rate SOCdriver 220 as shown in FIG. 7 by analyzing driving patterns such as acceleration, deceleration, braking, and steering of the driver. This is to determine the driver's battery charge rate optimized for each driver's driving habits by analyzing the driver's driving pattern, SOCdriver 220, basically set the SOCdriver (703) as a basic, a lot of rapid acceleration and braking, etc. Hybrid electric vehicles can meet the driver's needs by setting the SOCdriver (702,701) step by step higher. Drivers who prefer moderate acceleration and gentle braking can set the driver battery charge rate by setting the SOCdriver (704,705) slightly lower. The hybrid electric vehicle can be operated efficiently without difficulty. This can be set automatically by an algorithm, or it can be set by a manual switch such as Power 1, Power 2, Normal, Econo 1, Econo 2.

가중 배터리 충전율인 SOCweight(280)를 결정하는 SOCgps(210), SOCspeed(230), SOCdriver(220)의 조합방법은 수학적 연산, 논리연산, 회귀연산, 퍼지연산, 신경망 연산 방법 등 다양한 알고리즘을 사용할 수가 있다. 가중 배터리 충전율인 SOCweight(280)를 결정하는 함수 인자로 SOCgps, SOCspeed, SOCdriver 이외에도, 도 3과 같이 배터리 충방전 특성, 울트라캡 충방전 특성, 엔진출력특성, 발전기 출력특성, 구동 모터 출력특성, 연료전지 온도, 연료전지 습도, 연료전지 산소농도, 연료전지 수소농도 등(260)을 이용하여 가중 배터리 충전율 SOCweight(280) 와 예측 배터리 충전율 SOCpred(120)를 더 세밀하게 조절할 수 있다.SOCgps (210), SOCspeed (230), and SOCdriver (220) combination method for determining SOCweight (280), which is the weight of battery charge, can use various algorithms such as mathematical operation, logical operation, regression operation, fuzzy operation, and neural network operation method. have. In addition to SOCgps, SOCspeed, and SOCdriver as function parameters for determining the SOCweight 280, which is a weighted battery charge rate, the battery charge and discharge characteristics, ultracap charge and discharge characteristics, engine output characteristics, generator output characteristics, drive motor output characteristics, fuel as shown in FIG. The battery temperature, fuel cell humidity, fuel cell oxygen concentration, fuel cell hydrogen concentration, etc. 260 may be used to further control the weighted battery charge rate SOCweight 280 and the predicted battery charge rate SOCpred 120.

도 1: 기존의 하이브리드 전기자동차 시스템 Figure 1: Existing hybrid electric vehicle system

도 2: 본 발명에 의한 하이브리드 전기자동차 시스템 고안2 is a hybrid electric vehicle system design according to the present invention

도 3: 가중 배터리 충전율 SOCweight의 함수결정 방법 3: Method of Determining Weighted Battery Charge Rate SOCweight

도 4: SOCweight의 도로구배패턴 분석 FlowFigure 4: Flow gradient pattern analysis flow of SOCweight

도 5: GPS 배터리 충전율(SOCgps) 결정 로직Figure 5: GPS Battery Charge Rate (SOCgps) Determination Logic

도 6: 주행속도 배터리 충전율(SOCspeed) 결정하는 방법6: How to determine driving speed battery charge rate (SOCspeed)

도 7: 운전자 배터리 충전율(SOCdriver)을 결정하는 방법Figure 7: How to determine driver battery charge rate (SOCdriver)

Claims (7)

하이브리드 전기자동차의 배터리 충전율을 연산 함수식으로 결정하는데 있어서, 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)의 도로정보, 교통정보, 가속, 감속, 제동, 조향, 하중의 정보를 함수인자로 이용하여 배터리 충전율을 결정하고, In determining the battery charging rate of the hybrid electric vehicle as a calculation function, the function factor is the road information, traffic information, acceleration, deceleration, braking, steering and load information of the digital map supported by the GPS system. To determine the battery charge rate, 기본 배터리 충전율(SOCbas)과 가중 배터리 충전율(SOCweight)을 함수 인자로 이용하여 예측 배터리 충전율(SOCpred)를 결정하고,The predicted battery charge rate (SOCpred) is determined using the basic battery charge rate (SOCbas) and the weighted battery charge rate (SOCweight) as a function factor. GPS 배터리 충전율(SOCgps), 주행패턴 배터리 충전율(SOCspeed), 운전패턴 배터리 충전율(SOCdriver)의 함수식을 이용하여 상기 가중 배터리 충전율( SOCweight)를 결정하며,The weighted battery charge rate (SOCweight) is determined using a function formula of GPS battery charge rate (SOCgps), driving pattern battery charge rate (SOCspeed), driving pattern battery charge rate (SOCdriver), 상기 가중 배터리 충전율(SOCweight)은 배터리 충방전 특성, 울트라캡 충방전 특성, 엔진출력특성, 발전기 출력특성, 구동 모터 출력특성, 연료전지 온도, 연료전지 습도, 연료전지 산소농도, 연료전지 수소농도의 정보를 보조 함수인자로 이용하여 결정하는 것을 특징으로 하는 하이브리드 전기자동차용 배터리의 예측 제어 방법.The weighted battery charge rate (SOCweight) of the battery charge and discharge characteristics, ultracap charge and discharge characteristics, engine output characteristics, generator output characteristics, drive motor output characteristics, fuel cell temperature, fuel cell humidity, fuel cell oxygen concentration, fuel cell hydrogen concentration Predictive control method for a hybrid electric vehicle battery characterized in that the information is determined using the auxiliary function factor. 삭제delete 삭제delete 제1항에 있어서,The method of claim 1, 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)의 도로정보와 교통정보를 이용하여 주행하게 될 도로의 구배 및 교통정보를 기반으로 최적의 주행 에너지 생산, 소비, 및 분배를 예측하여 GPS 배터리 충전율인 SOCgps(210)를 결정하는 것이 특징인 하이브리드 전기자동차용 배터리의 예측 제어 방법.GPS is estimated by predicting optimal driving energy production, consumption, and distribution based on the gradient and traffic information of the road to be driven using the road information and traffic information of the digital map supported by the location information system (GPS). Predictive control method of a battery for a hybrid electric vehicle, characterized in that determining the SOCgps (210), the battery charge rate. 제1항에 있어서, The method of claim 1, 위치정보시스템(GPS)이 지원되는 전자지도(Digital Map)를 이용하여 목적지까지 주행하게 될 도로 종류에 따라서 고속도로, 일반도로, 도심주행의 평균 주행속도 및 주행속도의 변동률을 기반으로 예측되는 주행패턴 배터리 충전율인 SOCspeed(230)를 결정하는 것이 특징인 하이브리드 전기자동차용 배터리의 예측 제어 방법.The driving pattern predicted based on the average speed and the rate of change of the driving speed of highways, general roads, and urban driving according to the type of road to be driven to the destination using a digital map supported by the location information system (GPS). Predictive control method of a battery for a hybrid electric vehicle, characterized in that determining the SOCspeed (230) that is the battery charge rate. 제1항에 있어서,The method of claim 1, 운전자의 가속, 감속, 제동, 조향의 정도를 고려하여 운전패턴 배터리 충전율인 SOCdriver(220)를 결정하는 것이 특징인 하이브리드 전기자동차용 배터리의 예측 제어 방법.Predictive control method of a hybrid electric vehicle battery, characterized in that determining the driving pattern battery charge rate SOCdriver 220 in consideration of the degree of acceleration, deceleration, braking, steering of the driver. 삭제delete
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KR100949260B1 (en) * 2009-08-13 2010-03-25 정연종 Battery prediction control algorism for hybrid electric vehicle
KR101080059B1 (en) 2010-05-04 2011-11-07 주식회사 와이즈오토모티브 Apparatus and method for managing power of vehicle
KR101181032B1 (en) 2010-10-29 2012-09-07 기아자동차주식회사 Control device and method of battery charge and discharge in hybrid vehicle
US8330424B2 (en) 2009-11-17 2012-12-11 Hyundai Motor Company Battery's state-of-charge balancing control method for hybrid vehicle
KR101371463B1 (en) 2012-09-06 2014-03-24 기아자동차주식회사 Method and system for controlling recharging of a battery for hybrid vehicle
CN104309605A (en) * 2014-09-02 2015-01-28 郑州宇通客车股份有限公司 Hybrid electrical vehicle energy-saving control method based on GPS (global position system) geographic information
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KR101574137B1 (en) * 2014-04-03 2015-12-04 (주)진우소프트이노베이션 Apparatus for electric vehicle specific navigation adopting battery consumption weight algorithm according to the slope change of road and the method thereof
KR101583996B1 (en) * 2014-10-07 2016-01-21 주식회사 만도 Method for controlling battery of mild hybrid vehicle
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KR20210038127A (en) 2019-09-30 2021-04-07 현대자동차주식회사 Method of Predicting SOC Control Based On Variable Time and Vehicle Thereof
KR20210054409A (en) 2019-11-05 2021-05-13 주식회사 커넥토 Electric-Bus Telematics System

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Cited By (19)

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KR100949260B1 (en) * 2009-08-13 2010-03-25 정연종 Battery prediction control algorism for hybrid electric vehicle
WO2011019133A2 (en) * 2009-08-13 2011-02-17 Chung Yon Jong Battery-charging system for an electric vehicle
WO2011019133A3 (en) * 2009-08-13 2011-04-07 Chung Yon Jong Battery-charging system for an electric vehicle
US8330424B2 (en) 2009-11-17 2012-12-11 Hyundai Motor Company Battery's state-of-charge balancing control method for hybrid vehicle
KR101080059B1 (en) 2010-05-04 2011-11-07 주식회사 와이즈오토모티브 Apparatus and method for managing power of vehicle
KR101181032B1 (en) 2010-10-29 2012-09-07 기아자동차주식회사 Control device and method of battery charge and discharge in hybrid vehicle
KR101371463B1 (en) 2012-09-06 2014-03-24 기아자동차주식회사 Method and system for controlling recharging of a battery for hybrid vehicle
KR101509296B1 (en) * 2012-12-28 2015-04-08 주식회사 현대케피코 Battery charging method for hybrid vehicle
KR101574137B1 (en) * 2014-04-03 2015-12-04 (주)진우소프트이노베이션 Apparatus for electric vehicle specific navigation adopting battery consumption weight algorithm according to the slope change of road and the method thereof
US9616757B2 (en) 2014-05-20 2017-04-11 Hyundai Motor Company Method and apparatus for controlling regenerative braking of vehicle
CN104309605A (en) * 2014-09-02 2015-01-28 郑州宇通客车股份有限公司 Hybrid electrical vehicle energy-saving control method based on GPS (global position system) geographic information
KR101583996B1 (en) * 2014-10-07 2016-01-21 주식회사 만도 Method for controlling battery of mild hybrid vehicle
US9834199B2 (en) 2014-12-11 2017-12-05 Hyundai Motor Company Apparatus and method for controlling battery state of charge in hybrid electric vehicle
US9859723B2 (en) 2014-12-30 2018-01-02 Hanwha Land Systems Co., Ltd. Apparatus and method for adjusting charge/discharge range of vehicle based on events
US10011266B2 (en) 2016-01-07 2018-07-03 Hyundai Motor Company Method and controller for preventing over discharge of battery and hybrid vehicle thererby
KR20180045183A (en) * 2016-10-25 2018-05-04 현대자동차주식회사 appratus for controlling a driving mode for plug-in hybrid vehicle and a method the same
KR102370971B1 (en) 2016-10-25 2022-03-04 현대자동차주식회사 appratus for controlling a driving mode for plug-in hybrid vehicle and a method the same
KR20210038127A (en) 2019-09-30 2021-04-07 현대자동차주식회사 Method of Predicting SOC Control Based On Variable Time and Vehicle Thereof
KR20210054409A (en) 2019-11-05 2021-05-13 주식회사 커넥토 Electric-Bus Telematics System

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