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확률적 머신러닝 모델기반의 리튬이온배터리 파라미터 추정 알고리즘
김민호(Minho Kim),송민석(Minseok Song),임정택(Jeongtaek Lim),함경선(Kyung Sun Ham),이도헌(DOHEON LEE),김태형(Taehyoung Kim) 한국에너지학회 2024 에너지공학 Vol.33 No.1
In this study, a new lithium-ion battery performance degradation model and a stochastic machine learning model-based lithium-ion battery parameter estimation method were proposed and verified through actual battery degradation cycle experiment data. The proposed parameter estimation method based on a stochastic machine learning model requires less battery model operation time compared to other methods, enabling efficient parameter estimation. The lithium-ion battery performance degradation model is an equivalent circuit-based model, but it reflects various electrochemical phenomena, including side reactions on the surface of the anode active material, including the formation of a solid electrolyte interphase (SEI) layer, the loss of positive electrode active material due to mechanical stress-induced fatigue failure is included, and the corresponding decrease in the amount of cyclable lithium. In the proposed method of estimating the parameters of a lithium-ion battery model, a probabilistic machine learning model that can estimate battery model parameters from sensible data such as voltage and current is developed and used to generate virtual experiment data. We proposed a technique for learning and finding optimal battery model parameters based on the learned model. The developed performance degradation model and parameter estimation method were verified based on actual experimental data. Since it is impossible to observe the inside of the battery, correct answers to the battery parameters cannot be obtained, so the model and parameter estimation algorithm are indirectly verified through errors of voltage and temperature. As a result of the verification, the errors in voltage and temperature were found to be 0.676% and 0.207%, respectively.
마코프체인 몬테카를로 방법을 이용한 에너지 저장 장치용 배터리의 잔존 수명 추정
김동진(Dongjin Kim),김석구(Seok Goo Kim),최주호(Jooho Choi),송화섭(Hwa Seob Song),박상희(Sang Hui Park),이재욱(Jaewook Lee) 대한기계학회 2016 大韓機械學會論文集A Vol.40 No.10
리튬 이온 배터리의 잔존수명 추정은 품질보증, 운전계획, 교체주기 파악 등을 위해 활용된다는 점에서 그 필요성이 점점 커지고 있다. 본 논문에서는 에너지 저장 장치용 배터리의 잔존 수명을 단일지수 용량열화 모델과 마코프체인 몬테카를로(MCMC) 방법을 이용하여 추정한 결과를 제시한다. MCMC 방법은 사전 정보가 제대로 주어지지 않았을 때, 추정결과가 모델 초기값과 입력 설정값에 따라 크게 변하게 되는 단점이 있어, 실제 현장에서 배터리 모델과 추정법에 익숙하지 않은 사용자가 활용하는데 어려움이 있다. 이러한 어려움을 극복하기 위해, 본 논문에서는 베이지안 추론법의 이론식을 전역 탐색하여 구한 이론값과 MCMC 추정값을 비교해서, 초기값과 설정값을 결정하는 과정을 제안한다. Remaining useful life (RUL) estimation of the Li-ion battery has gained great interest because it is necessary for quality assurance, operation planning, and determination of the exchange period. This paper presents the RUL estimation of an Li-ion battery for an energy storage system using exponential function for the degradation model and Markov Chain Monte Carlo (MCMC) approach for parameter estimation. The MCMC approach is dependent upon information such as model initial parameters and input setting parameters which highly affect the estimation result. To overcome this difficulty, this paper offers a guideline for model initial parameters based on the regression result, and MCMC input parameters derived by comparisons with a thorough search of theoretical results.
박도현(Dohyun Park),송현식(Hyunsik Song),신동현(Donghyun Shin),김남욱(Namwook Kim) 한국자동차공학회 2019 한국 자동차공학회논문집 Vol.27 No.1
Battery Electric Vehicles(BEVs) are successfully penetrating the vehicle market with powerful motors and efficient battery systems, features that were unexpected by the consumers. In response to the successful market penetration, global automakers are sharply focusing on manufacturing their own brand names for BEVs. ‘Ioniq’ is the family name of electrified vehicles from Hyundai-Kia Motors. In this study, the Ioniq Electric Vehicle(EV) is tested on a chassis dynamometer, and the test results are analyzed to evaluate vehicle performance. Dedicated tests for a motor or a battery are preferred to examine the performance of the components. It not only requires well-organized testing facilities but also needs considerable time and effort to remove the components from the vehicle. Therefore, we equipped the electric vehicle with measuring devices and analyzed them to investigate vehicle performance and that of its components based on test results obtained from the chassis dynamometer tests. In addition, signals in On-Board Diagnostics(OBD) have been used to improve analysis. Based on the analysis, the accelerating performance and the efficiency of the vehicle were studied, and the characteristics of the motor and the battery were also investigated.
시뮬레이션 기반 전기자동차 1회 충전 주행거리 예측 및 IONIQ5 시험 성능 기반 검증
연제휘(Jehwi Yeon),박도현(Dohyun Park),이윤호(Yunho Lee),성혜인(Hyein Sung),임윤성(Yunsung Lim),이종태(Jong-Tae Lee),김남욱(Namwook Kim) 한국자동차공학회 2023 한국 자동차공학회논문집 Vol.31 No.4
Electric vehicles are gaining attention as eco-friendly vehicles because they do not generate exhaust gas the tailpipe. Recently, technology related to electric vehicles has been rapidly developing with the advances in battery and motor performance. As the number of electric vehicles released in the market continues to increase, government-affiliated institutions are requiring technology to predict the driving range of electric vehicles based on simulation to perform certification tasks efficiently. This study covers the simulated driving range of electric vehicles. We have shown an estimation of battery and motor parameters based on the MCT test data and detailed modeling of electric vehicles was conducted. We will verify the test results and simulation results using the MCT test data of Hyundai Motor’s IONIQ5.