Further Cost Reduction of Battery Manufacturing

The demand for batteries for energy storage is growing with the rapid increase in photovoltaics (PV) and wind energy installation as well as electric vehicle (EV), hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV). Electrochemical batteries have emerged as the preferred choice for most of the consumer product applications. Cost reduction of batteries will accelerate the growth in all of these sectors. Lithium-ion (Li-ion) and solid-state batteries are showing promise through their downward price and upward performance trends. We may achieve further performance improvement and cost reduction for Li-ion and solid-state batteries through reduction of the variation in physical and electrical properties. These properties can be improved and made uniform by considering the electrical model of batteries and adopting novel manufacturing approaches. Using quantum-photo effect, the incorporation of ultra-violet (UV) assisted photo-thermal processing can reduce metal surface roughness. Using in-situ measurements, advanced process control (APC) can help ensure uniformity among the constituent electrochemical cells. Industrial internet of things (IIoT) can streamline the production flow. In this article, we have examined the issue of electrochemical battery manufacturing of Li-ion and solid-state type from cell-level to battery-level process variability, and proposed potential areas where improvements in the manufacturing process can be made. By incorporating these practices in the manufacturing process we expect reduced cost of energy management system, improved reliability and yield gain with the net saving of manufacturing cost being at least 20%

Modelling of modular battery systems under cell capacity variation and degradation

We propose a simple statistical model of electrochemical cell degradation based on the general characteristics observed in previous large-scale experimental studies of cell degradation. This model is used to statistically explore the behaviour and lifetime performance of battery systems where the cells are organised into modules that are controlled semi-independently. Intuitively, such systems should offer improved reliability and energy availability compared to monolithic systems as the system ages and cells degrade and fail. To validate this intuition, this paper explores the capacity evolution of populations of systems composed of random populations of cells. This approach allows the probability that a given system design meets a given lifetime specification to be calculated. A cost model that includes the effect of uncertainty in degradation behaviour is introduced and used to explore the cost-benefit trade-offs arising from the interaction of degradation and module size. Case studies of an electric vehicle battery pack and a grid-connected energy storage system are used to demonstrate the use of the model to find lifetime cost-optimum designs. It is observed that breaking a battery energy storage system up into smaller modules can lead to large increases in accessible system capacity and may lead to a decision to use lower-quality, lower-cost cells in a cost-optimum system

Battery Aging Studies Based on Real-World Driving

While being a competitive candidate for energy storage systems in automotive applications, lithium-ion battery still needs to overcome fundamental compromises regarding energy density, power density, lifetime, costs and safety concerns. A significant breakthrough can be expected by understanding the real-world customer usage patterns and leveraging this knowledge to develop an optimized battery design and control. However, the challenges of filtering through massive real-world driving data and identifying the features relevant to the real-world battery operations still remain. This dissertation aims to bridge this gap by linking vehicle drive cycles to battery cell duty cycles, which enables quantifying the impacts of real-world variability on battery performance. In addition to performance and efficiency considerations, the methodology enables battery aging analysis in the context of optimal design and control of hybrid electric vehicles. This will facilitate design decisions that ensure adequate performance over the life span of the vehicle with considerations of the battery health objective. The novelty of this work lies in a more accurate method of synthetizing representative real-world drive cycles with a new algorithm to classify road and an innovative quantitative metric of driver style. A modified 48V mild hybrid vehicle model was built to relate the real-world drive cycles all the way to the battery cell duty cycles and to validate the impacts from driver aggressiveness on both the fuel efficiency and the battery loads. The cell duty cycles were further analyzed in frequency domain to synthesize characteristic cell test profiles representative of driver styles and road conditions. A battery cell cycle aging experiment was carried out using the synthesized test profiles. Results validate the positive correlation between driver aggressiveness and cell degradation, and further allow parameter identification of cell electro-chemical model. Modeling effort was extended to generate insights regarding the aging mechanisms, and calibrate a semi-empirical aging model. These tools will enable the inclusion of road conditions and driver styles into the development of battery pack design and propulsion system control hence improving the design assumption fidelity and real-world representativeness of the modeling approach

Manufacturing of Photovoltaic Devices, Power Electronics and Batteries for Local Direct Current Power Based Nanogrid

To meet the current and future demands of electrical power for household, industrial, commercial and transport sectors, the energy infrastructure has to undergo changes in terms of generation, distribution and consumption. Due to the shortcomings of nuclear and fossil fuel based power generation, the emergence of renewable energy has provided a very lucrative option. With the advent of low-cost photovoltaics (PV) panels and our ability to generate, store and use electrical energy locally without the need for long-range transmission, the world is about to witness transformational changes in electricity infrastructures. For local nano-grids, direct current (DC) -based system has several distinct advantages that are demonstrated through theoretical and experimental results. A PV- powered and local DC power based nano-grids can be more efficient, reliable, cyber secured, and can easily adopt internet of things (IoT) platforms. With DC generation, storage and consumption, significant amount of energy can be saved that are wasted in back and forth conversion between AC and DC. In case of geomagnetic disturbances, such nano-grids will be more resilient compared to centralized distribution network. Free-fuel, i.e. sunlight, based local DC nano-grid can be the sustainable and cost effective solution for underdeveloped, developing and developed economies. To take advantage of this, the manufacturing of PV, power electronics and batteries have to follow the best practices that aid process control, quality improvement and potential cost reduction. Without proper process control, the variation will result in yield loss, inferior performance and higher cost of production. On many instances, these issues were not considered, and some technology such as perovskite solar cell, received a lot of attention as a disruptive technology. Through detailed technical and economic assessments, it was shown that the variability and lack of rigorous process control will result in a lower efficiency when perovskite thin film solar cells are connected together to form a module. Due to stability and performance reasons, it was showed the perovskite solar cell is not ideal for 2-terminal or 4-terminal multi-junction/tandem configuration with silicon cells. Power electronics also play a vital role in PV systems. The challenges and design rules for silicon carbide (SiC) and gallium nitride (GaN) based power device manufacturing were analyzed. Based on it, advanced process control (APC) based single wafer processing (SWP) tools for manufacturing SiC and GaN power devices are proposed. For energy storage, batteries play an important role in PV installation. Li-ion technology will become the preferred storage due to its capabilities. Incorporation of advanced process control, rapid thermal processing, Industrial IoT, etc. can reduce variability, improve performance and reduce quality-check failures and bring down the cost of electrochemical batteries. The combined approaches in manufacturing of PV, power electronics and batteries will have a very positive impact in the growth of PV powered DC –based nano-grids

Managing End-of-Life Lithium-ion Batteries: an Environmental and Economic Assessment

The growing market for lithium-ion batteries raises concerns about sustainable management of those batteries at end of life. Launching relevant policies requires a comprehensive understanding of potential economic values as well as environmental performance of end-of-life lithium-ion batteries. However, both recyclers and policymakers are facing a number of unanswered questions, including 1) how battery technology trajectory would affect the incentives for recycling? 2) what strategies are available to improve material recovery efficiency? and 3) what is the potential for nanoparticle release during end-of-life processing, particularly for next-generation lithium-ion batteries who contain nano-scale cathode materials? This dissertation aims to fill these research gaps. Multi-criteria optimization modeling and fundamental material characterization methods were used to quantify environmental and economic trade-offs for end-of-life lithium-ion batteries. Results show that potential material recovery values decrease as battery cathode chemistry transitions to low-cost cathode materials, as a majority of potentially recoverable value resides in the base metals contained in the cathode. Cathode changes over time will result in a heavily co-mingled waste stream, further complicating waste management and recycling processes. An optimization model was developed to analyze the economic feasibility of recycling facilities under possible scenarios of waste stream volume and composition. Sensitivity analysis shows that the profitability is highly dependent on the expected mix of cathode chemistries in the waste stream and resultant variability in material mass and value. Estimated current collection rate of end-of-life lithium-ion batteries turned out to be extremely low, indicating more opportunities and higher profitability for local recycling facilities if this rate can be improved. Aiming to achieve segregation of high value metallic materials in lithium-ion batteries, a pre-recycling process, including mechanical shredding and size-based sorting steps, which can be easily scaled up to the industrial level, has been proposed. Sorting results show that contained metallic materials can be effectively segregated into size fractions at different levels. In addition, using this pre-recycling process as a case study, the nanoparticle exposure potential during mechanical processing has been proactively investigated by using both traditional and nano-enabled lithium-ion batteries. Results show that a substantial amount of nanoparticles released during the mechanical shredding but not the size-based sorting process. Additionally, shredding nano-scale LiFePO4 cathode batteries may have a higher potential for nanoparticle exposure. Facing the rapidly growing volume of spent lithium-ion batteries, the results suggest policy or other incentives may be necessary to promote a robust collection and recycling infrastructure as the economic incentives will likely decrease as the chemistry transitions away from cobalt-based cathodes. This dissertation also demonstrates the importance of implementing a battery labeling system as recyclers will likely face a co-mingled waste stream. Specifying recycling-relevant information would increase the effectiveness of the pre-recycling system

Data driven techniques for on-board performance estimation and prediction in vehicular applications.

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction.

A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs

Environmental and economic costs, benefits and uncertainties of vehicle electrification: a life cycle approach

Battery electric vehicles (BEVs) have been proposed as a pathway for reducing the environmental impacts of transportation systems. While BEVs are often referred to as zero-emission vehicles, production and operation consume resources and emit pollutants through the vehicle supply chain and generation of electricity for vehicle charging. Life cycle assessment is a standardized methodology for assessing the environmental impacts of product systems from a system-wide perspective; considering the total supply chain and the product life cycle from cradle-to-grave. However, conventional LCAs are often limited; based off static supply chain analysis, omitting system interactions or indirect effects, and insufficiently reflecting the underlying variability and uncertainty to support robust public policy decisions.The objective of this dissertation is to develop and refine methods of assessing the life cycle environmental impacts and economic costs of electric vehicle technologies and policies. The chapters of this dissertation make contributions in advancing spatial and temporal dynamics in LCA modelling, integrating vehicle operations with evolutions in technology, background systems, and product development, and offers novel estimates of the costs and emissions abatement potential of light and heavy duty electric vehicles. As shown herein, a systems perspective is required to estimate the environmental benefits and costs of vehicle electrification strategies. Efforts to achieve pollution abatement through technology change must address risks of leakage, substitution, and unintended environmental consequences