KR102629932B1 - Method of predicting battery performance of electric vehicles - Google Patents
Method of predicting battery performance of electric vehicles Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 28
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims abstract description 118
- 229910001416 lithium ion Inorganic materials 0.000 claims abstract description 118
- 238000004458 analytical method Methods 0.000 claims abstract description 53
- 238000007599 discharging Methods 0.000 claims abstract description 8
- 239000003792 electrolyte Substances 0.000 claims description 18
- 238000006243 chemical reaction Methods 0.000 claims description 11
- 239000002245 particle Substances 0.000 claims description 10
- 238000007086 side reaction Methods 0.000 claims description 8
- 230000016507 interphase Effects 0.000 claims description 5
- 239000007784 solid electrolyte Substances 0.000 claims description 5
- 238000009792 diffusion process Methods 0.000 claims description 3
- 238000003487 electrochemical reaction Methods 0.000 claims description 3
- 239000000463 material Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 6
- 238000002474 experimental method Methods 0.000 description 6
- 238000012795 verification Methods 0.000 description 3
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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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
- B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
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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
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
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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
- B60L2240/00—Control parameters of input or output; Target parameters
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- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/549—Current
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- B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
- B60Y2200/00—Type of vehicle
- B60Y2200/90—Vehicles comprising electric prime movers
- B60Y2200/91—Electric vehicles
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Abstract
본 발명은 전기차와 에너지 그리드가 상호 충-방전으로 연결된 상태에서, 상기 전기차의 리튬이온전지의 물리적 특성을 해석하는 전기적-열적 물리 해석단계, 상기 리튬이온전지의 교환전류밀도를 계산하는 전기화학적 수명 해석단계 및 상기 전기적-열적 물리 해석단계에 의해 해석된 상기 리튬이온전지의 정보를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 VxG 신호 입출력 해석단계를 포함하는 전기차의 배터리 성능 예측 방법으로서, 본 발명에 의하면, 물리적 기반 모델링을 이용한 수명예측 해석을 통해 다양한 충-방전 사이클 조건에서의 리튬이온전지의 성능을 예측하는 것을 그 목적이 있다.The present invention provides an electrical-thermal physical analysis step of analyzing the physical characteristics of the lithium-ion battery of the electric vehicle while the electric vehicle and the energy grid are connected through mutual charging and discharging, and an electrochemical lifespan of calculating the exchange current density of the lithium-ion battery. Based on the analysis step and the information of the lithium ion battery analyzed by the electrical-thermal physical analysis step, a VxG signal input/output analysis step of calculating performance according to the VxG (Vehicle to Grid) signal of the lithium ion battery, comprising: As a method for predicting battery performance of electric vehicles, the purpose of the present invention is to predict the performance of lithium-ion batteries under various charge-discharge cycle conditions through life prediction analysis using physically-based modeling.
Description
본 발명은 전기차의 배터리 수명을 예측하기 위한 방법에 관한 것이다.The present invention relates to a method for predicting battery life of an electric vehicle.
종래의 리튬이온전지 해석 관련 모델은 등가회로모델, 물리적기반모델, 통계기반모델 등 다양하게 존재한다.There are various models related to conventional lithium-ion battery analysis, such as equivalent circuit models, physically-based models, and statistical-based models.
등가회로모델은 복잡한 리튬이온전지의 반응 특성을 간단한 전자회로 부품으로 치환하여 배터리의 전기적 특성을 예측하며, 통계기반모델의 경우 배터리의 실험 데이터를 이용해 특정 상황에 대한 배터리 거동을 예측한다.The equivalent circuit model predicts the electrical characteristics of the battery by replacing the complex reaction characteristics of lithium-ion batteries with simple electronic circuit components, and the statistical-based model uses experimental data from the battery to predict battery behavior in specific situations.
상기 등가회로모델과 통계기반모델은 해석의 경제적 측면에서 강점이 있지만, 전지의 물리적 특성에 대한 해석이 부족하기 때문에 해석의 범용성과 정확성 측면에서의 한계가 있으며, 복잡한 충-방전 패턴에 대한 배터리 응답 및 수명 예측이 불가능하다.Although the above equivalent circuit model and statistical-based model have strengths in the economic aspect of analysis, they have limitations in terms of versatility and accuracy of analysis due to the lack of analysis of the physical characteristics of the battery, and battery response to complex charge-discharge patterns. and lifespan prediction is impossible.
반면, 물리적기반모델의 경우 배터리 내부 전기화학 및 열적 반응을 수식화하여 모델에서 해석하는 방식을 사용하며, 모델 구성이 복잡하지만 정확성 측면에서 가장 뛰어나다.On the other hand, physically-based models use a method of formulating the electrochemical and thermal reactions inside the battery and analyzing them in a model. Although the model configuration is complex, it is the best in terms of accuracy.
리튬이온전지의 준2차원 모델은 대표적인 물리적기반모델이며, 리튬이온전지를 구성하는 무수히 많은 3차원적 배터리 반응을 전극 도메인과 입자 도메인의 준2차원으로 구성하여 해석하기 때문에 물리적기반모델의 정확성과 해석의 경제성 측면에서 뛰어난 모델이다.The quasi-two-dimensional model of a lithium-ion battery is a representative physically-based model. Because it analyzes the countless three-dimensional battery reactions that make up a lithium-ion battery by constructing it in the quasi-two dimensions of the electrode domain and particle domain, the accuracy and accuracy of the physically-based model are guaranteed. It is an excellent model in terms of economic efficiency of analysis.
하지만 기존의 준2차원 모델 해석 활용 범위는 정전류-정전압 충-방전 시 리튬이온전지의 단순 성능 검증에 한정되어 있었으며, 복잡한 충-방전 신호에 대한 리튬이온전지 응답을 가능케 하는 일련의 모델 알고리즘 구성이 필요하다.However, the scope of existing quasi-2D model analysis was limited to simple performance verification of lithium-ion batteries during constant current-constant voltage charge-discharge, and a series of model algorithms that enable lithium-ion battery response to complex charge-discharge signals were required. need.
이상의 배경기술에 기재된 사항은 발명의 배경에 대한 이해를 돕기 위한 것으로서, 이 기술이 속하는 분야에서 통상의 지식을 가진 자에게 이미 알려진 종래기술이 아닌 사항을 포함할 수 있다.The matters described in the above background technology are intended to aid understanding of the background of the invention, and may include matters that are not prior art already known to those skilled in the art in the field to which this technology belongs.
본 발명은 전기자동차와 에너지 그리드를 상호 충-방전으로 연결하는 방식의 작동상황에서 리튬이온전지의 수명을 예측하기 위한 기술이다. 이를 위하여 전극 입자에서 전지 셀 전체까지 아우르는 전지 전반의 물리적 거동을 해석하는 다중차원 모델을 이용하여 리튬이온전지의 운전거동과 수명거동을 시뮬레이션한다. 본 모델은 리튬이온전지를 전극 입자 도메인, 전극 도메인으로 나누고, 각 도메인에서 물리적으로 주요한 영향을 가지는 물리방정식을 해석한다. 본 발명은 물리적 기반 모델링을 이용한 수명예측 해석을 통해 다양한 충-방전 사이클 조건에서의 리튬이온전지의 성능을 예측하는 것을 그 목적이 있다.The present invention is a technology for predicting the lifespan of a lithium-ion battery in an operating situation where an electric vehicle and an energy grid are connected through mutual charging and discharging. To this end, the operation and lifespan behavior of lithium-ion batteries are simulated using a multi-dimensional model that analyzes the physical behavior of the entire battery, from electrode particles to the entire battery cell. This model divides the lithium-ion battery into the electrode particle domain and the electrode domain, and analyzes the physical equations that have major physical effects in each domain. The purpose of the present invention is to predict the performance of lithium-ion batteries under various charge-discharge cycle conditions through life prediction analysis using physically-based modeling.
본 발명의 일 관점에 의한 전기차의 배터리 성능 예측 방법은, 전기차와 에너지 그리드가 상호 충-방전으로 연결된 상태에서, 상기 전기차의 리튬이온전지의 물리적 특성을 해석하는 전기적-열적 물리 해석단계, 상기 리튬이온전지의 교환전류밀도를 계산하는 전기화학적 수명 해석단계 및 상기 전기적-열적 물리 해석단계에 의해 해석된 상기 리튬이온전지의 정보를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 VxG 신호 입출력 해석단계를 포함한다.A method for predicting the battery performance of an electric vehicle according to one aspect of the present invention includes an electrical-thermal physics analysis step of analyzing the physical characteristics of the lithium ion battery of the electric vehicle while the electric vehicle and the energy grid are connected through mutual charging and discharging, the lithium Based on the information of the lithium ion battery analyzed by the electrochemical life analysis step for calculating the exchange current density of the ion battery and the electrical-thermal physical analysis step, according to the VxG (Vehicle to Grid) signal of the lithium ion battery It includes a VxG signal input/output analysis step to calculate performance.
여기서, 상기 전기적-열적 물리 해석단계는, 상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 단계 및 상기 리튬 이온량을 이용하여 상기 리튬이온전지의 상기 전극과 전해질 내의 과전압을 계산하는 단계를 포함한다.Here, the electrical-thermal physics analysis step includes calculating the amount of lithium ions in the electrode and electrolyte of the lithium ion battery and calculating the overvoltage in the electrode and electrolyte of the lithium ion battery using the amount of lithium ion. do.
그리고, 상기 전기화학적 수명 해석단계는, 상기 리튬 이온량 및 상기 과전압을 바탕으로 상기 리튬이온전지의 음극 입자 표면 SEI(Solid Electrolyte Interphase)층의 두께 및 저항을 계산하는 단계를 포함한다.In addition, the electrochemical life analysis step includes calculating the thickness and resistance of the solid electrolyte interphase (SEI) layer on the surface of the negative electrode particle of the lithium ion battery based on the amount of lithium ions and the overvoltage.
또한, 상기 전기화학적 수명 해석단계는, 상기 SEI층을 전기화학적 부반응으로 상정하여 교환전류밀도를 계산하는 단계를 더 포함한다.In addition, the electrochemical life analysis step further includes calculating the exchange current density by assuming the SEI layer as an electrochemical side reaction.
그리고, 상기 전기적-열적 물리 해석단계는, 상기 리튬 이온량 및 상기 과전압과 상기 교환전류밀도를 전기화학 반응 속도 이론에 따른 Butler-Volmer(버틀러-발머) 반응식에 적용하여 상기 리튬이온전지의 출력 전류, 전압, 전력을 계산하는 단계를 더 포함한다.And, in the electrical-thermal physical analysis step, the amount of lithium ions, the overvoltage, and the exchange current density are applied to the Butler-Volmer reaction equation according to electrochemical reaction rate theory to obtain the output current of the lithium ion battery, It further includes calculating voltage and power.
나아가, 상기 전기적-열적 물리 해석단계는, 상기 리튬이온전지의 출력 전류, 전압, 전력을 계산하는 단계 후 상기 리튬이온전지의 줄열(Joule heat)과 반응열을 계산하여, 상기 리튬이온전지의 평균온도를 계산하는 단계를 더 포함한다.Furthermore, the electrical-thermal physical analysis step calculates the Joule heat and heat of reaction of the lithium-ion battery after calculating the output current, voltage, and power of the lithium-ion battery, and calculates the average temperature of the lithium-ion battery. It further includes the step of calculating .
그리고, 상기 VxG 신호 입출력 해석단계는, 상기 리튬이온전지의 평균온도를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 단계를 포함한다.And, the VxG signal input/output analysis step includes calculating performance according to the VxG (Vehicle to Grid) signal of the lithium ion battery, based on the average temperature of the lithium ion battery.
또한, 상기 VxG 신호 입출력 해석단계는, 상기 리튬이온전지와 상기 에너지 그리드의 충전기 사이에서 전달되는 상기 VxG 신호를 전기적으로 수치화하여 다중차원 모델의 경계조건으로 전달하는 상기 리튬이온전지의 VxG 신호에 따른 전류, 전압, 전력정보 입력 단계를 더 포함한다.In addition, in the VxG signal input/output analysis step, the VxG signal transmitted between the lithium-ion battery and the charger of the energy grid is electrically quantified and transmitted as a boundary condition of the multi-dimensional model according to the VxG signal of the lithium-ion battery. It further includes steps for inputting current, voltage, and power information.
여기서, 상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 단계는 상기 VxG 신호를 기반으로 물질 확산 지배방정식(governing equation)을 통해 상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 것을 특징으로 한다.Here, the step of calculating the amount of lithium ions in the electrode and electrolyte of the lithium ion battery is characterized by calculating the amount of lithium ions in the electrode and electrolyte of the lithium ion battery through a material diffusion governing equation based on the VxG signal. Do it as
다음으로, 본 발명의 다른 일 관점에 의한 전기차의 배터리 성능 예측 방법은, 전기차와 에너지 그리드가 상호 충-방전으로 연결된 상태에서, 상기 전기차의 리튬이온전지와 상기 에너지 그리드의 충전기 사이에서 전달되는 VxG 신호를 전기적으로 수치화하여 다중차원 모델의 경계조건으로 전달하는 상기 리튬이온전지의 VxG 신호에 따른 전류, 전압, 전력정보 입력 단계, 상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 단계, 상기 리튬 이온량을 이용하여 상기 리튬이온전지의 상기 전극과 전해질 내의 과전압을 계산하는 단계, 상기 리튬 이온량 및 상기 과전압을 바탕으로 상기 리튬이온전지의 음극 입자 표면 SEI(Solid Electrolyte Interphase)층의 두께 및 저항을 계산하는 단계, 상기 SEI층을 전기화학적 부반응으로 상정하여 교환전류밀도를 계산하는 단계, 상기 리튬 이온량 및 상기 과전압과 상기 교환전류밀도를 이용하여, 상기 리튬이온전지의 출력 전류, 전압, 전력을 계산하는 단계, 상기 리튬이온전지의 줄열(Joule heat)과 반응열을 계산하여, 상기 리튬이온전지의 평균온도를 계산하는 단계 및 상기 리튬이온전지의 평균온도를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 단계를 포함한다.Next, the method for predicting the battery performance of an electric vehicle according to another aspect of the present invention is to measure the VxG transmitted between the lithium-ion battery of the electric vehicle and the charger of the energy grid in a state where the electric vehicle and the energy grid are connected through mutual charging and discharging. A step of inputting current, voltage, and power information according to the VxG signal of the lithium-ion battery, which electrically quantifies the signal and transmits it as a boundary condition of a multi-dimensional model, calculating the amount of lithium ions in the electrode and electrolyte of the lithium-ion battery, Calculating the overvoltage in the electrode and electrolyte of the lithium ion battery using the amount of lithium ions, calculating the thickness and resistance of the SEI (Solid Electrolyte Interphase) layer on the surface of the negative electrode particle of the lithium ion battery based on the amount of lithium ions and the overvoltage. Calculating the exchange current density by assuming the SEI layer as an electrochemical side reaction, calculating the output current, voltage, and power of the lithium ion battery using the amount of lithium ions, the overvoltage, and the exchange current density. calculating the average temperature of the lithium ion battery by calculating the Joule heat and heat of reaction of the lithium ion battery, and calculating the average temperature of the lithium ion battery based on the VxG (Vehicle to Grid) includes the step of calculating performance according to the signal.
본 발명의 전기차의 배터리 성능 예측 방법에 의하면, 다음과 같은 기술적인 기대효과가 있다.According to the method for predicting battery performance of an electric vehicle of the present invention, there are the following expected technical effects.
첫째, 전기자동차용 대형/대용량 리튬이온전지의 성능 및 수명예측 물리 기반 모델 확보가 가능하며,First, it is possible to secure a physics-based model for predicting the performance and lifespan of large/high-capacity lithium-ion batteries for electric vehicles.
둘째, V0G/V1G/V2G의 충-방전 조건에 대한 리튬이온전의 유효수명 단축을 예측할 수 있는 시뮬레이션 조건을 확보할 수 있고,Second, it is possible to secure simulation conditions that can predict the shortening of the effective life of lithium-ion batteries for charge-discharge conditions of V0G/V1G/V2G,
셋째, 모델 기반으로 리튬이온전지의 충-방전 패턴에 대한 수명예측과 배터리 수명 연장을 위한 V1G/V2G 패턴 도출 기술을 개발할 수 있다.Third, based on the model, it is possible to develop V1G/V2G pattern derivation technology to predict the lifespan of the charge-discharge pattern of lithium-ion batteries and extend battery life.
그리고, 산업-경제적 기대효과로는,And, as an expected industrial-economic effect,
첫째, 배터리 수명 예측과 평가를 통해 전기자동차 수요자가 V2G에 참여할 수 있도록 하는 충-방전 전략 등 운영 방안의 제시가 가능하며,First, through battery life prediction and evaluation, it is possible to present operational plans such as charge-discharge strategies that enable electric vehicle consumers to participate in V2G.
둘째, 리튬이온전지 충전 가용 범위, 최대 충전 상한 등의 V1G 및 V2G 충전 패턴 도출 요소에 대한 배터리 수명 영향을 파악하여 충-방전 운영 전략 수립을 가능하게 하며, 이에 따른 스마트그리드 충-방전 운영 시스템에 대한 국내-외 산업화 기반 구축에 도움을 줄 수 있다.Second, it is possible to establish a charge-discharge operation strategy by identifying the impact of battery life on V1G and V2G charging pattern derivation factors such as lithium-ion battery charging range and maximum charge upper limit, and accordingly, the smart grid charge-discharge operation system. It can help build a foundation for domestic and international industrialization.
도 1은 본 발명의 전기차의 배터리 성능 예측 방법을 순서적으로 도시한 것이다.
도 2는 본 발명에 의한 리튬이온전지 성능 관련 실험 및 모델 해석 결과이다.
도 3은 본 발명에 의한 리튬이온전지 수명 사이클 실험 및 모델 해석 결과이다.
도 4 내지 도 6은 V0G/V1G/V2G 운용 방식에 따른 배터리 응답 실험 및 모델 해석 결과 및 수명예측 결과이다.Figure 1 sequentially shows a method for predicting battery performance of an electric vehicle according to the present invention.
Figure 2 shows the results of experiments and model analysis related to lithium ion battery performance according to the present invention.
Figure 3 shows the results of a lithium-ion battery life cycle experiment and model analysis according to the present invention.
Figures 4 to 6 show battery response experiment and model analysis results and life prediction results according to V0G/V1G/V2G operation methods.
본 발명과 본 발명의 동작상의 이점 및 본 발명의 실시에 의하여 달성되는 목적을 충분히 이해하기 위해서는 본 발명의 바람직한 실시 예를 예시하는 첨부 도면 및 첨부 도면에 기재된 내용을 참조하여야만 한다.In order to fully understand the present invention, its operational advantages, and the objectives achieved by practicing the present invention, reference should be made to the accompanying drawings illustrating preferred embodiments of the present invention and the contents described in the accompanying drawings.
본 발명의 바람직한 실시 예를 설명함에 있어서, 본 발명의 요지를 불필요하게 흐릴 수 있는 공지의 기술이나 반복적인 설명은 그 설명을 줄이거나 생략하기로 한다.In describing preferred embodiments of the present invention, known techniques or repetitive descriptions that may unnecessarily obscure the gist of the present invention will be reduced or omitted.
도 1은 본 발명의 전기차의 배터리 성능 예측 방법을 순서적으로 도시한 것이다. 이하, 도 1을 참조하여 본 발명의 일 실시예에 의한 전기차의 배터리 성능 예측 방법을 설명하기로 한다.Figure 1 sequentially shows a method for predicting battery performance of an electric vehicle according to the present invention. Hereinafter, a method for predicting battery performance of an electric vehicle according to an embodiment of the present invention will be described with reference to FIG. 1.
본 발명에 의한 전기차의 배터리 성능 예측 방법은 전기자동차와 에너지 그리드를 상호 충-방전으로 연결하는 방식의 작동상황에서, 리튬이온전지의 수명을 예측하기 위한 기술에 대한 것이며, 전기적-열적 물리 해석단계(S100), 전기화학적 수명 해석단계(S200), 그리고 VxG(Vehicle to Grid) 신호 입출력 해석단계(S300를 포함한다.The method for predicting the battery performance of an electric vehicle according to the present invention is a technology for predicting the lifespan of a lithium-ion battery in an operating situation in which the electric vehicle and the energy grid are connected through mutual charging and discharging, and involves an electrical-thermal physical analysis step. (S100), electrochemical life analysis step (S200), and VxG (Vehicle to Grid) signal input/output analysis step (S300).
그리고, 전기적-열적 물리 해석단계(S100)는 전극 및 전해질 리튬 이온량 계산 단계(S110), 전극 및 전해질 과전압 분포 계산 단계(S120), 리튬이온전지 전류, 전압, 출력 계산 단계(S130), 리튬이온전지 평균 온도 계산 단계(S140)를 포함한다.In addition, the electrical-thermal physical analysis step (S100) includes the electrode and electrolyte lithium ion amount calculation step (S110), the electrode and electrolyte overvoltage distribution calculation step (S120), the lithium ion battery current, voltage, and output calculation step (S130), and the lithium ion It includes a battery average temperature calculation step (S140).
그리고, 전기화학적 수명 해석단계는(S200)는 음극 내부 SEI(Solid Electrolyte Interphase)층 두께 및 저항 계산 단계(S210)와, SEI층으로 인한 부반응 교환전류 계산 단계(S220)를 포함한다.In addition, the electrochemical life analysis step (S200) includes a step of calculating the thickness and resistance of the SEI (Solid Electrolyte Interphase) layer inside the cathode (S210) and a step of calculating the side reaction exchange current due to the SEI layer (S220).
그리고, VxG 신호 입출력 해석단계(S300)는 리튬이온전지의 VxG 신호에 따른 전류, 전압, 전력정보 입력 단계(S310)와, VxG 신호에 따른 리튬이온전지 성능 및 수명 계산 단계(S320)를 포함한다.In addition, the VxG signal input/output analysis step (S300) includes a step of inputting current, voltage, and power information according to the VxG signal of the lithium-ion battery (S310), and a step of calculating lithium-ion battery performance and lifespan according to the VxG signal (S320). .
먼저, 리튬이온전지의 VxG 신호에 따른 전류, 전압, 전력정보 입력 단계(S310)에서는 전기자동차의 리튬이온전지와 스마트 그리드 충전기 사이에서 전달되는 VxG 신호를 전기적으로 수치화하여 다중차원 모델의 경계조건으로 전달한다.First, in the step (S310) of inputting current, voltage, and power information according to the VxG signal of the lithium-ion battery, the VxG signal transmitted between the lithium-ion battery of the electric vehicle and the smart grid charger is electrically quantified and used as the boundary condition of the multidimensional model. Deliver.
그리고, 전극 및 전해질 리튬 이온량 계산 단계(S110)에서는 S310에서 입력된 충-방전 신호를 기반으로 물질 확산 지배방정식(governing equation)을 통해 전극 및 전해질에서의 리튬이온 농도 분포를 계산한다.Then, in the electrode and electrolyte lithium ion amount calculation step (S110), the lithium ion concentration distribution in the electrode and electrolyte is calculated through a material diffusion governing equation based on the charge-discharge signal input in S310.
다음, 전극 및 전해질 과전압 분포 계산 단계(S120)에서는 S110에서 구한 리튬 농도 분포를 이용해 리튬이온전지의 전극과 전해질 내의 과전압을 계산한다.Next, in the electrode and electrolyte overvoltage distribution calculation step (S120), the overvoltage in the electrode and electrolyte of the lithium ion battery is calculated using the lithium concentration distribution obtained in S110.
그리고, 음극 내부 SEI층 두께 및 저항 계산 단계(S210)에서는 S110과 S120의 결과를 바탕으로 리튬이온전지의 수명 열화에 가장 큰 영향을 미치는 음극 입자 표면 SEI층 두께 및 저항을 계산한다.Then, in the step of calculating the thickness and resistance of the SEI layer inside the negative electrode (S210), the thickness and resistance of the SEI layer on the surface of the negative electrode particle, which has the greatest influence on the deterioration of the lifespan of the lithium ion battery, are calculated based on the results of S110 and S120.
그래서, SEI층으로 인한 부반응 교환전류 계산 단계(S220)에서는 S210에서 구한 음극 입자 표면의 SEI층을 전기화학적 부반응으로 상정하여 그에 따른 교환전류밀도를 계산한다.Therefore, in the step of calculating the side reaction exchange current due to the SEI layer (S220), the SEI layer on the surface of the cathode particle obtained in S210 is assumed to be an electrochemical side reaction and the exchange current density is calculated accordingly.
다음, 리튬이온전지 전류, 전압, 출력 계산 단계(S130)에서는 S110, S120, SEI층으로 인한 부반응 교환전류 계산 단계(S220)의 결과를 전기화학 반응 속도 이론에 따른 Butler-Volmer(버틀러-발머) 반응식에 적용하여 리튬이온전지의 출력 전류, 전압, 전력을 계산한다.Next, in the lithium-ion battery current, voltage, and output calculation step (S130), the results of the side reaction exchange current calculation step (S220) due to S110, S120, and SEI layers are calculated using Butler-Volmer according to electrochemical reaction rate theory. Apply the reaction formula to calculate the output current, voltage, and power of the lithium-ion battery.
그리고, 리튬이온전지 평균 온도 계산 단계(S140)에서는 S110 및 S120, S130의 결과를 바탕으로 리튬이온전지 전반의 줄열(Joule heat)과 반응열을 계산하여 리튬이온전지의 평균온도를 계산한다.Then, in the lithium-ion battery average temperature calculation step (S140), the average temperature of the lithium-ion battery is calculated by calculating the Joule heat and reaction heat of the entire lithium-ion battery based on the results of S110, S120, and S130.
마지막으로, VxG 신호에 따른 리튬이온전지 성능 및 수명 계산 단계(S320)에서는 전기적-열적 물리 해석단계(S100)에서 계산한 리튬이온전지 정보를 바탕으로 리튬이온전지의 VxG 신호에 따른 성능 및 수명을 계산한다.Lastly, in the lithium-ion battery performance and lifespan calculation step (S320) according to the VxG signal, the performance and lifespan according to the VxG signal of the lithium-ion battery are calculated based on the lithium-ion battery information calculated in the electrical-thermal physics analysis step (S100). Calculate.
도 2는 본 발명에 의한 리튬이온전지 성능 관련 실험 및 모델 해석 결과이고, 도 3은 본 발명에 의한 리튬이온전지 수명 사이클 실험 및 모델 해석 결과이다. 그리고, 도 4 내지 도 6은 V0G/V1G/V2G 운용 방식에 따른 배터리 응답 실험 및 모델 해석 결과 및 수명예측 결과이다.Figure 2 shows the results of experiments and model analysis related to lithium ion battery performance according to the present invention, and Figure 3 shows the results of lithium ion battery life cycle experiments and model analysis according to the present invention. And, Figures 4 to 6 show battery response experiment and model analysis results and life prediction results according to V0G/V1G/V2G operation methods.
이상과 같은 본 발명에 의한 리튬이온 단전지 물리기반 상태 및 수명 모델에 의한 단순 방전 및 충방전사이클에 대한 해석을 수행하였다. 첫 번째로, 도 2와 같이 충방전율에 관한 0.3Crate 조건의 단순 방전을 4.16-3.0V 사이에서 해석을 수행하여, 성능시험데이터와 모델 결과를 비교하였다.Analysis of simple discharge and charge/discharge cycle was performed using the physics-based state and life model of the lithium-ion single cell according to the present invention as described above. First, as shown in Figure 2, a simple discharge analysis was performed between 4.16 and 3.0V under the 0.3Crate condition regarding charge/discharge rate, and the performance test data and model results were compared.
실험값 대비 모델링 오차율은 최대 1.54%, 평균 0.53%이다. 방전 초기, 1% 내의 오차율을 보이고, 방전 말기, 실험값 대비 오차율이 증가한다. 목표치인 오차율 5% 이내를 만족한다.The modeling error rate compared to the experimental value is a maximum of 1.54% and an average of 0.53%. At the beginning of discharge, the error rate is within 1%, and at the end of discharge, the error rate increases compared to the experimental value. The target error rate is within 5%.
앞서 확보한 성능관련 변수를 기반으로 수명 모델링을 수행 후, 도 3과 같이 수명시험데이터(4psi 압착모듈)를 통해 수명 관련 변수들을 확보하여 수명 신뢰성을 검증하였다.After performing lifespan modeling based on the performance-related variables previously obtained, lifespan reliability was verified by securing lifespan-related variables through lifespan test data (4psi compression module) as shown in FIG. 3.
실험값 대비 모델링 오차율은 최대 1.54%, 평균 0.53%이다. 방전 초기, 1% 내의 오차율을 보이고, 방전 말기, 실험값 대비 오차율이 증가한다. 목표치인 오차율 5% 이내를 만족한다.The modeling error rate compared to the experimental value is a maximum of 1.54% and an average of 0.53%. At the beginning of discharge, the error rate is within 1%, and at the end of discharge, the error rate increases compared to the experimental value. The target error rate is within 5%.
성능, 수명 관련 모델 변수들을 확보 후, 도 4 내지 도 6에서 참조되는 바와 같이, 신뢰성 검증을 마친 모델을 통해 V0G/V1G/V2G 해석을 수행하였다.After securing model variables related to performance and lifespan, V0G/V1G/V2G analysis was performed using the model that completed reliability verification, as shown in Figures 4 to 6.
V0G는 최대 허용치 전력을 일방적으로 계속 공급하는 방식이며, V1G는 전력망 상태에 따라 충전 속도를 조절하는 제어형 충전 방식이며, V2G는 전기차에서 전력망으로 전력이 흐르는 양방향 충방전으로 진화하는 방식을 의미한다.V0G is a method that unilaterally continues to supply the maximum allowable power, V1G is a controlled charging method that adjusts the charging speed according to the status of the power grid, and V2G refers to a method that evolves into a bidirectional charging and discharging method in which power flows from the electric vehicle to the power grid.
실험결과 데이터와 비교는 첫 번째 사이클, 성능곡선의 모양이 아닌 전체 운용시간을 기준으로 진행하였다. 그 결과, V1G>V2G>V0G 순서로 오차율이 적었다. VxG 성능 모델과 수명 모델의 검증(오차율 5%)을 마쳤다.Comparison with experimental result data was conducted based on the total operating time, not the first cycle or the shape of the performance curve. As a result, the error rate was low in the order V1G>V2G>V0G. Verification of the VxG performance model and lifespan model (error rate 5%) was completed.
EV 배터리 1cycle은 대략 900W, 600Km 성능을 보인다. 목표치인 300cycle(18만Km)을 기준으로 VxG의 수명 모델링을 진행하였다. V0G/V1G/V2G 순서대로 300cycle 기준 모델링 결과, SOH(그림20)는 82.92% / 78.18% / 77.68%으로 전기자동차 배터리의 보증 SOH인 70%를 만족한다. V0G와 V1G의 SOH차이는 4.74%. V1G와 V2G의 SOH차이는 0.5%. V0G에 비해 V1G/V2G가 수명감소가 5%정도 더 진행되지만, V1G와 V2G의 수명차이는 크지 않았다.One cycle of EV battery has a performance of approximately 900W and 600km. Lifespan modeling of VxG was conducted based on the target value of 300 cycles (180,000 km). As a result of modeling based on 300 cycles in the order of V0G/V1G/V2G, SOH (Figure 20) is 82.92% / 78.18% / 77.68%, which satisfies the guaranteed SOH of 70% for electric vehicle batteries. The SOH difference between V0G and V1G is 4.74%. The SOH difference between V1G and V2G is 0.5%. Compared to V0G, V1G/V2G's lifespan decreases by about 5%, but the difference in lifespan between V1G and V2G is not significant.
이상과 같이 본 발명은 리튬이온전지를 전극 입자 도메인, 전극 도메인으로 나누고, 각 도메인에서 물리적으로 주요한 영향을 가지는 물리방정식을 해석하여, 물리적 기반 모델링을 이용한 수명예측 해석을 통해 다양한 충-방전 사이클 조건에서의 리튬이온전지의 성능을 예측할 수가 있다.As described above, the present invention divides the lithium ion battery into the electrode particle domain and the electrode domain, analyzes the physical equations that have a major physical influence in each domain, and analyzes life expectancy using physics-based modeling to predict various charge-discharge cycle conditions. The performance of lithium-ion batteries can be predicted.
이상과 같은 본 발명은 예시된 도면을 참조하여 설명되었지만, 기재된 실시 예에 한정되는 것이 아니고, 본 발명의 사상 및 범위를 벗어나지 않고 다양하게 수정 및 변형될 수 있음은 이 기술의 분야에서 통상의 지식을 가진 자에게 자명하다. 따라서 그러한 수정 예 또는 변형 예들은 본 발명의 특허청구범위에 속한다 하여야 할 것이며, 본 발명의 권리범위는 첨부된 특허청구범위에 기초하여 해석되어야 할 것이다.Although the present invention as described above has been described with reference to the illustrative drawings, it is not limited to the described embodiments, and it is common knowledge in the field of this technology that various modifications and changes can be made without departing from the spirit and scope of the present invention. It is self-evident to those who have it. Accordingly, such modifications or variations should be considered to fall within the scope of the patent claims of the present invention, and the scope of rights of the present invention should be interpreted based on the appended claims.
S100 : 전기적-열적 물리 해석단계
S200 : 전기화학적 수명 해석 단계
S300 : VxG 신호 입출력 해석단계S100: Electrical-thermal physics analysis stage
S200: Electrochemical life analysis step
S300: VxG signal input/output analysis stage
Claims (10)
상기 리튬이온전지의 교환전류밀도를 계산하는 전기화학적 수명 해석단계; 및
상기 전기적-열적 물리 해석단계에 의해 해석된 상기 리튬이온전지의 정보를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 VxG 신호 입출력 해석단계를 포함하는,
전기차의 배터리 성능 예측 방법.An electrical-thermal physical analysis step of analyzing the physical characteristics of the lithium-ion battery of the electric vehicle while the electric vehicle and the energy grid are connected to each other through mutual charging and discharging;
An electrochemical life analysis step of calculating exchange current density of the lithium ion battery; and
Comprising a VxG signal input/output analysis step of calculating performance according to the VxG (Vehicle to Grid) signal of the lithium ion battery based on the information of the lithium ion battery analyzed by the electrical-thermal physical analysis step,
Method for predicting battery performance of electric vehicles.
상기 전기적-열적 물리 해석단계는,
상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 단계; 및
상기 리튬 이온량을 이용하여 상기 리튬이온전지의 상기 전극과 전해질 내의 과전압을 계산하는 단계를 포함하는,
전기차의 배터리 성능 예측 방법.In claim 1,
The electrical-thermal physical analysis step is,
Calculating the amount of lithium ions in the electrode and electrolyte of the lithium ion battery; and
Comprising the step of calculating the overvoltage within the electrode and electrolyte of the lithium ion battery using the amount of lithium ions,
Method for predicting battery performance of electric vehicles.
상기 전기화학적 수명 해석단계는,
상기 리튬 이온량 및 상기 과전압을 바탕으로 상기 리튬이온전지의 음극 입자 표면 SEI(Solid Electrolyte Interphase)층의 두께 및 저항을 계산하는 단계를 포함하는,
전기차의 배터리 성능 예측 방법.In claim 2,
The electrochemical life analysis step is,
Comprising the step of calculating the thickness and resistance of the SEI (Solid Electrolyte Interphase) layer on the surface of the negative electrode particle of the lithium ion battery based on the amount of lithium ions and the overvoltage.
Method for predicting battery performance of electric vehicles.
상기 전기화학적 수명 해석단계는,
상기 SEI층을 전기화학적 부반응으로 상정하여 교환전류밀도를 계산하는 단계를 더 포함하는,
전기차의 배터리 성능 예측 방법.In claim 3,
The electrochemical life analysis step is,
Further comprising calculating the exchange current density by assuming the SEI layer as an electrochemical side reaction,
Method for predicting battery performance of electric vehicles.
상기 전기적-열적 물리 해석단계는,
상기 리튬 이온량 및 상기 과전압과 상기 교환전류밀도를 전기화학 반응 속도 이론에 따른 Butler-Volmer(버틀러-발머) 반응식에 적용하여 상기 리튬이온전지의 출력 전류, 전압, 전력을 계산하는 단계를 더 포함하는,
전기차의 배터리 성능 예측 방법.In claim 4,
The electrical-thermal physical analysis step is,
Calculating the output current, voltage, and power of the lithium ion battery by applying the amount of lithium ions, the overvoltage, and the exchange current density to the Butler-Volmer reaction equation according to electrochemical reaction rate theory. ,
Method for predicting battery performance of electric vehicles.
상기 전기적-열적 물리 해석단계는,
상기 리튬이온전지의 출력 전류, 전압, 전력을 계산하는 단계 후 상기 리튬이온전지의 줄열(Joule heat)과 반응열을 계산하여, 상기 리튬이온전지의 평균온도를 계산하는 단계를 더 포함하는,
전기차의 배터리 성능 예측 방법.In claim 5,
The electrical-thermal physical analysis step is,
After calculating the output current, voltage, and power of the lithium ion battery, calculating the Joule heat and heat of reaction of the lithium ion battery, further comprising calculating the average temperature of the lithium ion battery,
Method for predicting battery performance of electric vehicles.
상기 VxG 신호 입출력 해석단계는,
상기 리튬이온전지의 평균온도를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 단계를 포함하는,
전기차의 배터리 성능 예측 방법.In claim 6,
The VxG signal input/output analysis step is,
Comprising the step of calculating performance according to the VxG (Vehicle to Grid) signal of the lithium ion battery, based on the average temperature of the lithium ion battery,
Method for predicting battery performance of electric vehicles.
상기 VxG 신호 입출력 해석단계는,
상기 리튬이온전지와 상기 에너지 그리드의 충전기 사이에서 전달되는 상기 VxG 신호를 전기적으로 수치화하여 다중차원 모델의 경계조건으로 전달하는 상기 리튬이온전지의 VxG 신호에 따른 전류, 전압, 전력정보 입력 단계를 더 포함하는,
전기차의 배터리 성능 예측 방법.In claim 7,
The VxG signal input/output analysis step is,
A step of inputting current, voltage, and power information according to the VxG signal of the lithium ion battery, in which the VxG signal transmitted between the lithium ion battery and the charger of the energy grid is electrically quantified and transmitted as a boundary condition of a multidimensional model, is further added. containing,
Method for predicting battery performance of electric vehicles.
상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 단계는 상기 VxG 신호를 기반으로 물질 확산 지배방정식(governing equation)을 통해 상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 것을 특징으로 하는,
전기차의 배터리 성능 예측 방법.In claim 8,
The step of calculating the amount of lithium ions in the electrode and electrolyte of the lithium ion battery is characterized in that the amount of lithium ions in the electrode and electrolyte of the lithium ion battery is calculated through a material diffusion governing equation based on the VxG signal. ,
Method for predicting battery performance of electric vehicles.
상기 리튬이온전지의 전극 및 전해질의 리튬 이온량을 계산하는 단계;
상기 리튬 이온량을 이용하여 상기 리튬이온전지의 상기 전극과 전해질 내의 과전압을 계산하는 단계;
상기 리튬 이온량 및 상기 과전압을 바탕으로 상기 리튬이온전지의 음극 입자 표면 SEI(Solid Electrolyte Interphase)층의 두께 및 저항을 계산하는 단계;
상기 SEI층을 전기화학적 부반응으로 상정하여 교환전류밀도를 계산하는 단계;
상기 리튬 이온량 및 상기 과전압과 상기 교환전류밀도를 이용하여, 상기 리튬이온전지의 출력 전류, 전압, 전력을 계산하는 단계;
상기 리튬이온전지의 줄열(Joule heat)과 반응열을 계산하여, 상기 리튬이온전지의 평균온도를 계산하는 단계; 및
상기 리튬이온전지의 평균온도를 바탕으로, 상기 리튬이온전지의 VxG(Vehicle to Grid) 신호에 따른 성능을 계산하는 단계를 포함하는,
전기차의 배터리 성능 예측 방법.With the electric vehicle and the energy grid connected through mutual charging and discharging, the lithium-ion battery electrically quantifies the VxG signal transmitted between the lithium-ion battery of the electric vehicle and the charger of the energy grid and transmits it as a boundary condition of a multidimensional model. inputting current, voltage, and power information according to the VxG signal;
Calculating the amount of lithium ions in the electrode and electrolyte of the lithium ion battery;
calculating an overvoltage within the electrode and electrolyte of the lithium ion battery using the amount of lithium ions;
calculating the thickness and resistance of a solid electrolyte interphase (SEI) layer on the surface of the negative electrode particles of the lithium ion battery based on the amount of lithium ions and the overvoltage;
Calculating exchange current density by assuming the SEI layer as an electrochemical side reaction;
calculating the output current, voltage, and power of the lithium ion battery using the amount of lithium ions, the overvoltage, and the exchange current density;
Calculating Joule heat and heat of reaction of the lithium ion battery to calculate an average temperature of the lithium ion battery; and
Comprising the step of calculating performance according to the VxG (Vehicle to Grid) signal of the lithium ion battery, based on the average temperature of the lithium ion battery,
Method for predicting battery performance of electric vehicles.
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