SoC estimation for lithium-ion batteries : review and future challenges

ABSTRACT: Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence

Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods in advanced BMSs. This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs. First, the widely explored battery models in the existing literature are classified into physics-based electrochemical models and electrical equivalent circuit models. Second, a general state-space representation that describes electrical dynamics of a faulty battery is presented. The formulation of the state vectors and the identification of the parameter matrices are then elaborated. Third, the fault mechanisms of both battery faults (incl. overcharege/overdischarge faults, connection faults, short circuit faults) and sensor faults (incl. voltage sensor faults and current sensor faults) are discussed. Furthermore, different types of modeling uncertainties, such as modeling errors and measurement noises, aging effects, measurement outliers, are elaborated. An emphasis is then placed on the observer design (incl. online state observers and offline state observers). The algorithm implementation of typical state observers for battery fault diagnosis is also put forward. Finally, discussion and outlook are offered to envision some possible future research directions.Comment: Submitted to Renewable and Sustainable Energy Reviews on 09-Jan-202

Modelling and estimation of vanadium redox flow batteries: a review

Redox flow batteries are one of the most promising technologies for large-scale energy storage, especially in applications based on renewable energies. In this context, considerable efforts have been made in the last few years to overcome the limitations and optimise the performance of this technology, aiming to make it commercially competitive. From the monitoring point of view, one of the biggest challenges is the estimation of the system internal states, such as the state of charge and the state of health, given the complexity of obtaining such information directly from experimental measures. Therefore, many proposals have been recently developed to get rid of such inconvenient measurements and, instead, utilise an algorithm that makes use of a mathematical model in order to rely only on easily measurable variables such as the system’s voltage and current. This review provides a comprehensive study of the different types of dynamic models available in the literature, together with an analysis of the existing model-based estimation strategies. Finally, a discussion about the remaining challenges and possible future research lines on this field is presented.The research that gave rise to these results received support from “la Caixa” Foundation (ID 100010434. Fellowship code LCF/BQ/DI21/11860023) , the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094, CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+ project TRE2103000), the Spanish Ministry of Science and Innovation (project PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033 / ERDF,EU) and the Spanish Ministry of Economy and Competitiveness under Project DOVELAR (ref. RTI2018-096001-B-C32).Peer ReviewedPostprint (published version

불확실성 하에서 시스템의 유지 보수 최적화 및 수명 주기 예측

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2019. 2. 이원보.The equipment and energy systems of most chemical plants have undergone repetitive physical and chemical changes and lead to equipment failure through aging process. Replacement and maintenance management at an appropriate point in time is an important issue in terms of safety, reliability and performance. However, it is difficult to find an optimal solution because there is a trade-off between maintenance cost and system performance. In many cases, operation companies follow expert opinions based on long-term industry experience or forced government policy. For cost-effective management, a quantitative state estimation method and management methodology of the target system is needed. Various monitoring technologies have been introduced from the field, and quantifiable methodologies have been introduced. This can be used to diagnose the current state and to predict the life span. It is useful for decision making of system management. This thesis propose a methodology for lifetime prediction and management optimization in energy storage system and underground piping environment. First part is about online state of health estimation algorithm for energy storage system. Lithium-ion batteries are widely used from portable electronics to auxiliary power supplies for vehicle and renewable power generation. In order for the battery to play a key role as an energy storage device, the state estimation, represented by state of charge and state of health, must be well established. Accurate rigorous dynamic models are essential for predicting the state-of health. There are various models from the first principle partial differential model to the equivalent circuit model for electrochemical phenomena of battery charge / discharge. It is important to simulate the battery dynamic behavior to estimate system state. However, there is a limitation on the calculation load, therefore an equivalent circuit model is widely used for state estimation. Author presents a state of health estimation algorithm for energy storage system. The proposed methodology is intended for state of health estimation under various operating conditions including changes in temperature, current and voltage. Using a recursive estimator, this method estimate the current battery state variable related to battery cell life. State of health estimation algorithm uses estimated capacity as a cell life-time indicator. Adaptive parameters are calibrated by a least sum square error estimation method based on nonlinear programming. The proposed state-of health estimation methodology is validated with cell experimental lithium ion battery pack data under typical operation schedules and demonstration site operating data. The presented results show that the proposed method is appropriate for state of health estimation under various conditions. The suitability of algorithm is demonstrated with on and off line monitoring of new and aged cells using cyclic degradation experiments. The results from diverse experimental data and data of demonstration sites show the appropriateness of the accuracy, robustness. Second part is structural reliability model for quantification about underground pipeline risk. Since the long term usage and irregular inspection activities about detection of corrosion defect, catastrophic accidents have been increasing in underground pipelines. Underground pipeline network is a complex infrastructure system that has significant impact on the economic, environmental and social aspects of modern societies. Reliability based quantitative risk assessment model is useful for underground pipeline involving uncertainties. Firstly, main pipeline failure threats and failure modes are defined. External corrosion is time-dependent factor and equipment impact is time-independent factor. The limit state function for each failure cause is defined and the accident probability is calculated by Monte Carlo simulation. Simplified consequence model is used for quantification about expected failure cost. It is applied to an existing underground pipeline for several fluids in Ulsan industrial complex. This study would contribute to introduce quantitative results to prioritize pipeline management with relative risk comparisons Third part is maintenance optimization about aged underground pipeline system. In order to detect and respond to faults causing major accidents, high resolution devices such as ILI(Inline inspection), Hydrostatic Testing, and External Corrosion Direct Assessment(ECDA) can be used. The proposed method demonstrates the structural adequacy of a pipeline by making an explicit estimate of its reliability and comparing it to a specified reliability target. Structural reliability analysis is obtaining wider acceptance as a basis for evaluating pipeline integrity and these methods are ideally suited to managing metal corrosion damage as identified risk reduction strategies. The essence of this approach is to combine deterministic failure models with maintenance data and the pipeline attributes, experimental corrosion growth rate database, and the uncertainties inherent in this information. The calculated failure probability suggests the basis for informed decisions on which defects to repair, when to repair them and when to re-inspect or replace them. This work could contribute to state estimation and control of the lithium ion battery for the energy storage system. Also, maintenance optimization model helps pipeline decision-maker determine which integrity action is better option based on total cost and risk.화학공장 내 장치 및 에너지 시스템은 반복적인 사용으로 물리화학적 변화를 겪으며 노후화되고 설계 수명에 가까워지게 된다. 적절한 시점에 장비 교체와 보수 관리는 안전과 신뢰도, 전체 시스템 성능을 좌우하는 중요한 문제이다. 그러나, 보수 비용과 시스템 성능을 유지하는 것 사이에는 트레이드 오프가 존재하기 때문에 이에 대한 최적점을 찾는 것은 어려운 문제이다. 많은 경우에 운영회사에서는 경험에 기반한 전문가 의견을 따르거나, 정부차원의 안전관리 정책 최소 기준에 맞추어 진행한다. 비용효율적 관리를 위하여 정량적인 상태 추정 기법이나 유지보수 관리 방법론은 필요하다. 많은 모니터링 기술이 개발되어지고 있고 점차 실시간 측정 방법이나 센서 기술이 발달 하고 있다. 그러나, 여전히 직접 측정 및 검사 이전 장비의 수명 예측과 시스템 관리에 대한 의사결정을 도울 방법론은 부족한 실정이다. 본 논문에서는 리튬 이온 배터리의 수명예측 방법론과 지하매설배관의 관리 최적화 문제를 다룬다. 첫 장에서는 에너지 저장시스템 운전패턴에 적합한 배터리 SOH 추정 방법론에 대한 것이다. 리튬 이온 배터리는 이동가능 전자장치에서부터 자동차 및 신재생발전 등의 보조 전력 저장장치로서 활용이 이루어지고 있다. 배터리가 정상적인 역할을 하기 위하여 SOC와 SOH의 정확한 추정이 중요하다. 정확한 동적 모델은 SOH 예측을 위하여 필수적이다. BMS에는 계산 로드에 한계가 있기 때문에 상태 추정을 위하여 계산 부하가 비교적 적은 등가회로 모델이 사용된다. 본 논문에서는 SOH 예측 알고리즘을 제시하고, 셀 및 실증 사이트 데이터로 검증한다. 반복 예측기와 관측기 기법을 활용하여 SOH를 추정을 통하여 현재의 배터리 상태를 제시한다. SOH 예측 알고리즘은 용량을 중요 상태변수로 하여 예측된다. 제안 알고리즘에서는 SOH를 정확히 추정하기 위하여 확장칼만필터를 도입하여 배터리 상태변수들을 정확히 예측하고 이를 기반으로 SOH를 추정하는 알고리즘을 제안한다. 두번째 장은 구조 신뢰도 분석을 통하여 지하배관의 정량적 위험성 모델을 수립한다. 배관의 장기 사용과 불규칙한 검사/보수 활동에 대한 불확실성은 지하배관 안전 사고의 위험성을 증대시키는 요인이다. 산업단지 내의 지하배관 네트워크는 복잡한 인프라를 갖추고 있기 때문에 사고 발생시 경제적, 환경적, 사회적으로 큰 위협요소가 된다. 신뢰도 기반 정량적 위험도 모델은 지하배관의 큰 불확실성 요소를 고려하는데 유용한 방법론이다. 배관 사고 위협요인과 사고 모드를 정의하고, 부식과 타공사에 이르는 시간 의존적, 비의존적 요소를 고려하여 한계상태함수를 결정한다. 몬테카를로 시뮬레이션을 통하여 연간 사고확률이 유추되며 사고 영향거리 및 누출량 계산 모델과 합하여 정량적 위험성 분석을 할 수 있다. 배관에 존재하는 다양한 물질들에 대하여 케이스 스터디를 진행하여 정량화된 위험도에 근거하여 배관관리 우선순위를 정하는 의사결정에 반영할 수 있다. 세번째 장은 노후화된 배관 시스템의 관리 최적화에 대한 내용이다. 사고의 위험성을 미연에 방지하기 위하여 다양한 검사, 보수 방법론이 사용된다. 그러나, 이에 대한 효과가 위험성과 어떻게 관련되어서 나타나는지 알기 어렵다. 대부분 경험적으로 혹은 제도적인 방안을 통하여 보수적인 안전관리를 진행하는 한계성이 있다. 제안된 방법론을 토대로 하여 안전관리 방법에 대한 실제적인 부식 관리에 영향 정도를 정량화 한다. 신뢰도 목표와 제안 되어진 예산 등을 제한조건으로 하는 최적화를 실시하여 최적의 검사 주기, 최적의 검사 방법론을 확인한다. 위 연구를 토대로 개선된 리튬이온 배터리의 온라인 상태추정 알고리즘 제시하고 위험도 환산 비용을 결합한 구조 신뢰도 모델로 지하배관 관리 최적화 방법론을 제시한다.Abstract i Contents vi List of Figures ix List of Tables xii CHAPTER 1. Introduction 14 1.1. Research motivation 14 1.2. Research objectives 19 1.3. Outline of the thesis 20 CHAPTER 2. Lithium ion battery modeling and state of health Estimation 21 2.1. Background 21 2.2. Literature Review 22 2.2.1. Battery model 23 2.2.2. Qualitative comparative review of state of health estimation algorithm 29 2.3. Previous estimation algorithm 32 2.3.1. Nonlinear State estimation method 32 2.3.2. Sliding mode observer 35 2.3.3. Proposed Algorithm 37 2.3.4. Uncertainty Factors for SOH estimation in ESS 42 2.4. Data acquisition 44 2.4.1. Lithium ion battery specification 45 2.4.2. ESS Experimental setup 47 2.4.3. Sensitivity Analysis for Model Parameter 54 2.5. Result and Discussion 59 2.5.1. Estimation results of battery model 59 2.5.2. Estimation results of proposed method 63 2.6. Conclusion 68 CHAPTER 3. Reliability estimation modeling for quantitative risk assessment about underground pipeline 69 3.1. Introduction 69 3.2. Uncertainties in underground pipeline system 72 3.3. Probabilistic based Quantitative Risk Assessment Model 73 3.3.1. Structural Reliability Assessment 73 3.3.2. Failure mode 75 3.3.3. Limit state function and variables 79 3.3.4. Reliability Target 86 3.3.5. Failure frequency modeling 90 3.3.6. Consequence modeling 95 3.3.7. Simulation method 101 3.4. Case study 103 3.4.1. Statistical review of Industrial complex underground pipeline 103 3.5. Result and discussion 107 3.5.1. Estimation result of failure probability 107 3.5.1. Estimation result validation 118 CHAPTER 4. Maintenance optimization methodology for cost effective underground pipeline management 120 4.1. Introduction 120 4.2. Problem Definition 124 4.3. Maintenance scenario analysis modeling 126 4.3.1. Methodology description 128 4.3.2. Cost modeling 129 4.3.3. Maintenance mitigation model 132 4.4. Case study 136 4.5. Results 138 4.5.1. Result of optimal re-inspection period 138 4.5.2. Result of optimal maintenance actions 144 CHAPTER 5. Concluding Remarks 145 References 147Docto

Modeling and control of fuel cell-battery hybrid energy sources

Environmental, political, and availability concerns regarding fossil fuels in recent decades have garnered substantial research and development in the area of alternative energy systems. Among various alternative energy systems, fuel cells and batteries have attracted significant attention both in academia and industry considering their superior performances and numerous advantages. In this dissertation, the modeling and control of these two electrochemical sources as the main constituents of fuel cell-battery hybrid energy sources are studied with ultimate goals of improving their performance, reducing their development and operational costs and consequently, easing their widespread commercialization. More specifically, Paper I provides a comprehensive background and literature review about Li-ion battery and its Battery Management System (BMS). Furthermore, the development of an experimental BMS design testbench is introduced in this paper. Paper II discusses the design of a novel observer for Li-ion battery State of Charge (SOC) estimation, as one of the most important functionalities of BMSs. Paper III addresses the control-oriented modeling and analysis of open-cathode fuel cells in order to provide a comprehensive system-level understanding of their real-time operation and to establish a basis for control design. Finally, in Paper IV a feedback controller, combined with a novel output-injection observer, is designed and implemented for open-cathode fuel cell temperature control. It is shown that temperature control not only ensures the fuel cell temperature reference is properly maintained, but, along with an uncertainty estimator, can also be used to adaptively stabilize the output voltage --Abstract, page iv

Industrial applications of the Kalman filter:a review

International audienc

Modelling and estimation in lithium-ion batteries: a literature review

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.This research received support from the Spanish Ministry of Science and Innovation under projects MAFALDA (PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ ERDF,EU) and MASHED (TED2021-129927B-I00), and by FI Joan Oró grant (code 2023 FI-1 00827), cofinanced by the European Union.Peer ReviewedPostprint (published version

A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle‐adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries.

The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the mathematical expression of different structural characteristics, and an improved reduce particle-adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current varying conditions, according to which the hybrid pulse power characterization test is accommodated. As can be known from the experimental results, the polynomial fitting treatment is carried out by conducting the curve fitting treatment and the maximum estimation error of the closed-circuit-voltage is 0.48% and its state of charge estimation error is lower than 0.30% in the hybrid pulse power characterization test, which is also within 2.00% under complex current varying working conditions. The iterate calculation process is conducted for the unmanned aerial vehicle lithium ion batteries together with the compound equivalent modeling, realizing its adaptive power state estimation and safety protection effectively

A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states

This paper presents a systematic review for the most commonly used lumped-parameter equivalent circuit model structures in lithium-ion battery energy storage applications. These models include the Combined model, Rint model, two hysteresis models, Randles' model, a modified Randles' model and two resistor-capacitor (RC) network models with and without hysteresis included. Two variations of the lithium-ion cell chemistry, namely the lithium-ion iron phosphate (LiFePO4) and lithium nickel-manganese-cobalt oxide (LiNMC) are used for testing purposes. The model parameters and states are recursively estimated using a nonlinear system identification technique based on the dual Extended Kalman Filter (dual-EKF) algorithm. The dynamic performance of the model structures are verified using the results obtained from a self-designed pulsed-current test and an electric vehicle (EV) drive cycle based on the New European Drive Cycle (NEDC) profile over a range of operating temperatures. Analysis on the ten model structures are conducted with respect to state-of-charge (SOC) and state-of-power (SOP) estimation with erroneous initial conditions. Comparatively, both RC model structures provide the best dynamic performance, with an outstanding SOC estimation accuracy. For those cell chemistries with large inherent hysteresis levels (e.g. LiFePO4), the RC model with only one time constant is combined with a dynamic hysteresis model to further enhance the performance of the SOC estimator