전기화학모델 기반 배터리 제어
Battery State Estimation Using an Electrochemical Model
- 주제(키워드) Battery State Estimation , Electrochemical Model
- 발행기관 아주대학교
- 지도교수 신치범
- 발행년도 2016
- 학위수여년월 2016. 2
- 학위명 박사
- 학과 및 전공 일반대학원 화학공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000022038
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
his paper reports the development of a battery model and its parameter estimator that are readily applicable to automotive battery management systems (BMSs). Due to the parameter estimator, the battery model can maintain reliability over the wider and longer use of the battery. To this end, the electrochemical model is used, which can reflect the aging-induced physicochemical changes in the battery to the aging-relevant parameters within the model. To update the effective kinetic and transport parameters using a computationally light BMS, the parameter estimator is built based on a covariance matrix adaptation evolution strategy (CMA-ES) that can function without the need for complex Jacobian matrix calculations. The existing CMA-ES implementation is modified primarily by region-based memory management such that it satisfies the memory constraints of the BMS. Among the several aging-relevant parameters, only the liquid-phase diffusivity of Li-ion is chosen to be estimated. This also facilitates integrating the parameter estimator into the BMS because a smaller number of parameter estimates yields the fewer number of iterations, thus, the greater computational efficiency of the parameter estimator. Consequently, the BMS-integrated parameter estimator enables the voltage to be predicted and the capacity retention to be estimated within 1% error throughout the battery life-time.
more목차
CHAPTER 1. INTRODUCTION 1
1.1 Background 1
1.2 Literature Review 3
1.3 Problem Statement 5
1.4 Our Approach 6
CHAPTER 2. BATTERY MODEL DEVELOPMENT 11
2.1 Model Formulation 11
2.1.1 Electrochemical Model 11
2.1.2 Model Reformulation 22
2.1.3 Model Verification 30
2.2 Model Implementation 35
2.2.1 Solution Methods 35
2.2.2 Solving Process 36
2.2.3 Solver Optimization 37
2.2.4 Implementation Detail 41
2.3 Model Validation 46
CHAPTER 3. PARAMETER ESTIMATOR DEVELOPMENT 54
3.1 Parameter Estimator Design 54
3.2 Parameter Estimator Validation 62
3.2.1 Offline Works 62
3.2.2 Online Works 78
CHAPTER 4. CONCLUSION 86
4.1 Summary 86
4.2 Future Research 87
REFERENCES 89
