전기적 특성을 고려한 전기자동차 버스바 신뢰성 기반 최적설계

국문 초록 (Abstract)

전동화라는 변화의 물결 아래에서 전기자동차 시장경쟁은 기존 내연기관 기반의 자동차 제조업체와 새로운 전기자동차 제조업체의 등장으로 과열되고 있다. 이러한 측면에서 버스바는 전기자동차 배터리시스템 내 고전압 연결을 위한 핵심 구성요소이자, 원가절감이 필요한 부품이다. 본 연구에서는 목적함수에 대한 강건성과 제한함수에 대한 신뢰성을 모두 고려한 신뢰성 기반 최적설계(Reliability Based Design Robust Optimization, RBRDO)를 통해, 전기자동차의 안정성과 원가경쟁력을 높이고자 한다.
본 논문에서는 전기자동차의 충돌에 따른 안정성을 고려한 기계적인 특성을 연구모델에 반영하였고, 방대한 수치 및 계산 비용을 줄이기 위해 다구찌 방법에 따른 신호 대 잡음비와 분산분석 그리고 중심합성계획법에 따른 반응표면 근사모델을 활용하였다. 통계적 안정성을 위해 제한조건 함수에 99.865%의 신뢰도를 적용하였고, 목적함수에는 외부 노이즈에 대한 강건성을 부여해 신뢰성 기반 강건 최적설계를 진행하였다. 결과적으로 안전계수를 일괄적으로 반영한 기존 다중목적 최적설계 결과와 비교하였을 때, 3번 설계변수는 -8.33%, 6번 설계변수는 +11.38%, 목적함수인 무게와 온도는 각각 -7.11%, +3.5%만큼 변화하여, 총 목적함수가 1.34% 증가했다. 해당 결과는 안전계수를 반영하지 않은 결과에 비해 신뢰성과 강건성이 확보된 보수적인 설계를 제공하면서, 안전계수(0.1)를 반영한 결과와 비교하면 무게를 -7.11%만큼 줄여 원가절감이 실현되었다.
해당 연구를 통해 전기자동차 배터리시스템의 특성을 반영한 신뢰성 기반 강건 최적설계를 제안하며, 버스바의 설계 및 검증 과정에서 충돌에 따른 안정성, 목적함수에 대한 강건성과 제한함수에 대한 신뢰성이 모두 고려된 원가절감을 실현할 것으로 기대한다.

다국어 초록 (Multilingual Abstract)

The global electric vehicle (EV) market is contending with increased competition from emerging and established automakers, shifting from traditional internal combustion engine vehicles. The electrical busbar, crucial for high-voltage connections in the battery system, plays a vital role in this evolution. This study responds to the market dynamics by employing reliability-based robust design optimization (RBRDO) methods to enhance EV cost competitiveness, considering the design robustness of objective functions and the reliability of constraints.
The article integrates mechanical properties into the design model, focusing on enhancing EV safety, especially in collision scenarios. Utilizing the signal-to-noise ratio with the Taguchi method and a surface response model using central composite design, the approach reduces costs by minimizing the number of design variables and required experiments. RBRDO ensures performance within a confidence level of 99.865%, diminishing performance variability resulting from uncertainties in design variables and parameters. A subsequent comparison with multi-objective design optimization (MODO) employing a safety factor reveals a 7.11% decrease in the first objective function (weight) and a 3.5% increase in the second objective function (temperature), resulting in a 1.34% increase in the total objective function.
This study proposes electrical busbar optimization incorporating mechanical properties, aiming for cost competitiveness while ensuring EV safety, design reliability, and robustness throughout the design stages and validation processes.

목차 (Table of Contents)

• 그림 차례 ········································································································································ii
• 표 차례 ···········································································································································iii
• 국문 요약 ·······································································································································iv
• 제1장 서론 ······································································································································1
• 제2장 배경 ······································································································································5
• 2.1. 설계 목적 ····························································································································5
• 2.2. 버스바 종류 및 연구모델 ································································································6
• 2.3. 신뢰성 기반 최적설계 이론 ····························································································7
• 2.4. 신뢰성 기반 강건 최적설계 이론 ··················································································9
• 제3장 실험계획법 및 다중목적 최적설계 ··············································································11
• 3.1. 유한요소해석 ····················································································································11
• 3.2. 다구찌 방법 ······················································································································12
• 3.3. 중심합성계획법 및 반응표면 근사모델 ······································································15
• 3.4. 다중목적 최적설계 결과 ································································································17
• 제4장 신뢰성 기반 강건 최적설계 ··························································································20
• 4.1. 신뢰성 기반 최적설계 결과 ··························································································20
• 4.2. 신뢰성 기반 강건 최적설계 결과 ················································································22
• 4.3. 비교 및 분석 ····················································································································23
• 제5장 결론 ····································································································································26
• 참고문헌 ········································································································································28
• ABSTRACT ·································································································································32