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Advances in Battery Manufacturing, Service, and Management Systems

Cover Image Copyright Year: 2017
Author(s): Jingshan Li; Shiyu Zhou; Yehui Han
Publisher: Wiley-IEEE Press
Content Type : Books & eBooks
Topics: Components, Circuits, Devices & Systems ;  Engineered Materials, Dielectrics & Plasmas
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Abstract

Addresses the methodology and theoretical foundation of battery manufacturing, service and management systems (BM2S2), and discusses the issues and challenges in these areas

This book brings together experts in the field to highlight the cutting edge research advances in BM2S2 and to promote an innovative integrated research framework responding to the challenges. There are three major parts included in this book: manufacturing, service, and management. The first part focuses on battery manufacturing systems, including modeling, analysis, design and control, as well as economic and risk analyses.  The second part focuses on information technology’s impact on service systems, such as data-driven reliability modeling, failure prognosis, and service decision making methodologies for battery services. The third part addresses battery management systems (BMS) for control and optimization of battery cells, opera ions, and hybrid storage systems to ensure overall performance and safety, as well as EV management.  The contributors consist of experts from universities, industry research centers, and government agency. In addition, this book:

  • Provides comprehensive overviews of lithium-ion battery and battery electrical vehicle manufacturing, as well as economic returns and government support
  • Introduces integrated models for quality propagation and productivity improvement, as well as indicators for bottleneck identification and mitigation in battery manufacturing
  • Covers models and diagnosis algorithms for battery SOC and SOH estimation, data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH
  • Presents mathematical models and novel structure of battery equalizers in battery management systems (BMS)
  • Reviews the state of the art of battery, supercapacitor, and battery-supercapacitor hybrid energy st rage systems (HESSs) for advanced electric vehicle applications

Advances in Battery Manufacturing, Services, and Management Systems is written for researchers and engineers working on battery manufacturing, service, operations, logistics, and management. It can also serve as a reference for senior undergraduate and graduate students interested in BM2S2.

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      Front Matter

      Copyright Year: 2017

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      The prelims comprise: Series Page Half¿¿?Title Page Title Page Copyright Page Contents Preface Contributors View full abstract»

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      LITHIUM¿¿?ION BATTERY MANUFACTURING FOR ELECTRIC VEHICLES: A CONTEMPORARY OVERVIEW

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      Battery electric vehicles (BEVs) fall into one of the following four categories: hybrid electric vehicle (HEV), plug¿¿?in electric vehicle (PHEV), extended range electric vehicle (EREV), and pure BEV. This chapter reviews different formats and structures of Li¿¿?ion battery cells, modules, and packs in BEVs. It focuses on the characteristics relevant to the joining, assembly, and packaging rather than the battery chemistries, functions, and performances. A number of BEV manufactures use pouch cells for lightweighting, better volumetric energy density, and high spatial efficiency. The chapter reviews a few selected joining technologies that are pertinent to Li¿¿?on battery cell and pack manufacturing, that is, ultrasonic welding, resistance welding, laser welding, wire bonding, and mechanical joining. It discusses the advantages and limitations as well as the applications in major BEVs. The chapter also discusses the battery manufacturing processes and particularly the joining processes. The chapter focuses on the manufacturing processes most relevant to BEVs in today's marketplace. View full abstract»

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      IMPROVING BATTERY MANUFACTURING THROUGH QUALITY AND PRODUCTIVITY BOTTLENECK INDICATORS

      Copyright Year: 2017

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      Battery manufacturing has attracted more research attention due to the increasing demand of alternative energy source for hybrid and electric vehicles. The coupling between productivity and quality in battery manufacturing is exhibited in various ways. This chapter considers a battery assembly system consisting of a serial production line with multiple inspections and repairs. It investigates the strong correlation between quality failure rate and throughput of conforming batteries existing in such systems, and introduces an integrated model of productivity and quality. The chapter develops analytical methods to estimate the production rate of conforming batteries based on quality flow model and overlapping decomposition approach. The complex system is decomposed into a set of overlapped serial lines and further analysis in serial lines is carried out based on aggregation procedures. The chapter also investigates bottleneck identification methods based on machine blockage and starvation probabilities and inspecting statistics. View full abstract»

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      EVENT¿¿?BASED MODELING FOR BATTERY MANUFACTURING SYSTEMS USING SENSOR DATA

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      An integrated modeling and analysis approach is much needed that can relate the sensor data with the dynamic battery manufacturing and evaluate the system performance for continuous improvement. This chapter presents such a method for sensor¿¿?enabled battery manufacturing system modeling and analysis. Distributed sensing, a system¿¿?wide deployment of sensing devices, has resulted in a data¿¿?rich environment with opportunities and challenges in manufacturing systems. On the basis of the system description and assumptions, an event¿¿?based modeling (EBM) approach for virtual multilayer sensor structure is introduced to reflect the market demand¿¿?driven battery manufacturing systems. The EBM method does not solely focus on physical model nor it is based on long¿¿?term steady state, but it closely communicates with distributed sensor networks for smart monitoring and diagnosis. The cost in manufacturing system is categorized into two categories: resources that are supplied as used (and needed) and resources that are supplied in advance of usage. View full abstract»

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      A REVIEW ON END¿¿?OF¿¿?LIFE BATTERY MANAGEMENT: CHALLENGES, MODELING, AND SOLUTION METHODS

      Copyright Year: 2017

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      End¿¿?of¿¿?life (EOL) battery packs may have significant residual value for secondary use and are too expensive to simply recycle. Quality variation adds complexity to EOL decision¿¿?making among multiple EOL options including disposal, recycling, remanufacturing, and reuse in secondary applications. The aim of the modeling and analysis for battery¿¿?remanufacturing systems is to determine the battery's optimal EOL decisions that maximize cost savings or minimize life cycle cost. This chapter considers the quality variation of EOL returns, resulting in different EOL decisions as well as different routes of remanufacturing processes and inventory level. It discusses some of the basics of battery remanufacturing, including the economic and ecological benefits, principles, operational strategy, and processes of battery remanufacturing. Several emerging issues of battery remanufacturing have been discussed with a main focus on modeling and analysis of electric vehicles (EVs) battery¿¿?remanufacturing system as well as decision¿¿?making related to the remanufacturing processes, inventory control, and reassembly strategy. View full abstract»

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      AN ANALYTICS APPROACH FOR INCORPORATING MARKET DEMAND INTO PRODUCTION DESIGN AND OPERATIONS OPTIMIZATION

      Copyright Year: 2017

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      This chapter focuses on the cost of manufacturing a product with a supporting discussion of the methods of management science analytics, which may be deployed for production design and operations optimization. It discusses the notion of optionality for several different purposes, namely, to identify equipment portfolio, design line throughput, purposefully select bottleneck(s), engineer product flexibility, and manage the inventory of work in process (WIP) and finished goods (FG) inventory under the uncertainty of commercial demand modeled endogenously with the plant operations. The chapter reviews discrete¿¿?event simulation and probabilistic financial methodology, which we have deployed to characterize an energy storage manufacturing process at General Electric. The goal is to manage throughput, inventory, expense, and fulfillment with an emphasis on the financial ramifications of production engineering and plant operational decision support. The chapter discusses the market and production coupling and treat product design as one of the distinguishing features of an alternative configuration. View full abstract»

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      PROGNOSTIC CLASSIFICATION PROBLEM IN BATTERY HEALTH MANAGEMENT

      Copyright Year: 2017

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      Batteries are crucial power sources for many engineering systems. Once the power source fails, the whole system completely stops performing its intended function. To avoid such catastrophic consequences, effective data¿¿?driven prognostic tools that can accurately predict the failure in advance are highly desired. Effective failure prognosis plays an important role in maintenance decision¿¿?making. Traditionally, most existing prognostic methods focus on the remaining useful life (RUL) prediction. Recently proposed prognostic methods have capability of real¿¿?time online model updating. This chapter compares the logistic regression method and joint prognostic model (JPM), which is one of the most advanced data¿¿?driven prognostic methods. It discusses a potential issue of having imbalanced data to provide important insights into the practitioners. The first category of methods typically models the time¿¿?to¿¿?failure data using parametric distributions such as Weibull distribution. The performance evaluation criteria used in the chapter are AUC, accuracy, and sensitivity. View full abstract»

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      A BAYESIAN APPROACH TO BATTERY PROGNOSTICS AND HEALTH MANAGEMENT

      Copyright Year: 2017

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      This chapter deals with each of three components of battery health management (BHM): battery state estimation, battery prognostics, and decision making. The state¿¿?of¿¿?health (SOH) of batteries is a measurement that reflects the general condition of a battery and its ability to deliver the specified performance compared to a fresh battery. The chapter discusses how to determine the remaining useful life (RUL) of a battery. State estimation techniques, like the extended Kalman filter (EKF), have been applied for real¿¿?time prediction of state¿¿?of¿¿?charge (SOC) and SOH of automotive batteries. A decision¿¿?level fusion of data¿¿?driven algorithms, like Autoregressive Integrated Moving Average (ARIMA) and neural networks, have been investigated for both diagnostics and prognostics. Particle filters (PFs) are a novel class of nonlinear filters that combine Bayesian learning techniques with importance sampling to provide good state tracking performance while keeping the computational load tractable. View full abstract»

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      RECENT RESEARCH ON BATTERY DIAGNOSTICS, PROGNOSTICS, AND UNCERTAINTY MANAGEMENT

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      This chapter provides a state¿¿?of¿¿?the¿¿?art review for Li¿¿?ion battery diagnostics, prognostics, and uncertainty management. It illustrates battery models used for battery state¿¿?of¿¿?charge (SOC) and state¿¿?of¿¿?health (SOH) estimation and reviews various estimation algorithms. The chapter elaborates data¿¿?driven prognostics for predicting the remaining useful life (RUL) of battery SOC and SOH. In particular, a Copula¿¿?based sampling method is explained in detail for predicting the RUL of the capacity fade. The chapter describes various uncertainties in battery diagnostics and prognostics and a proposed framework is illustrated for managing the battery model parameter uncertainty and model uncertainty in a systematic manner. Battery models can be classified into two groups: electrochemical models and equivalent circuit models (ECMs). Five types of uncertainty play a key role for reliable estimation of the battery performances of interest and they can be classified as measurement uncertainty, algorithm uncertainty, environmental uncertainty, model parameter uncertainty, and model uncertainty. View full abstract»

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      LITHIUM¿¿?ION BATTERY REMAINING USEFUL LIFE ESTIMATION BASED ON ENSEMBLE LEARNING WITH LS¿¿?SVM ALGORITHM

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      This chapter proposes the integrated use of parameter¿¿?selection¿¿?based ensemble learning (PSBEL) and least square support vector machine (LS¿¿?SVM) algorithm for lithium¿¿?ion battery remaining useful life (RUL) estimation. Technically, given a set of monitoring parameters, some groups of parameters are randomly selected to construct LS¿¿?SVM submodels. On the basis of these submodels, ensemble learning is utilized to achieve a final result, which overcomes the difficulty of accurately determining the model parameters and significantly improves the precision and stability of RUL estimation. The proposed approach provides practitioners with confidence intervals and probability distributions of RUL estimates for uncertainty management. The validity and applicability of the PSBEL with LS¿¿?SVM for RUL estimation are demonstrated using capacity¿¿?changing data of lithium¿¿?ion batteries during discharging cycles. To analyze the impact of different submodels on the precision of final RUL prediction and to improve the uncertainty representation, more advanced ensemble learning techniques should be considered. View full abstract»

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      DATA¿¿?DRIVEN PROGNOSTICS FOR BATTERIES SUBJECT TO HARD FAILURE

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      This chapter considers data¿¿?driven remaining useful life (RUL) of a battery prediction, which is typically made on the basis of projecting the trajectory of the system's health indicator, often called the degradation signal. Two most commonly used health indicators of batteries are capacity and internal resistance, while other health¿¿?dependent variables such as battery self¿¿?discharge rate may also be considered. By analyzing the evolution paths of the health indicating variables/degradation signals, it is possible to infer not only the current but also the future health status of the unit being studied. The chapter introduces a method specifically developed for battery RUL prediction under hard failure. In this method, a joint modeling scheme is used to take into consideration both the degradation data and the time¿¿?to¿¿?failure data. To better assess the performance of the prognostic algorithm, alternative interval prediction, the maximum power interval (MPI), is introduced as opposed to confidence intervals and mean/median¿¿?based intervals. View full abstract»

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      REVIEW OF BATTERY EQUALIZERS AND INTRODUCTION TO THE INTEGRATED BUILDING BLOCK DESIGN OF DISTRIBUTED BMS

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      Battery equalizers are brought forward to reduce imbalance between battery cells and thus improve the overall performance. Equalization has been a very important part of advanced battery management system (BMS). Many equalization techniques have been investigated based on the connection type of the equalizers, such as series, parallel, or series¿¿?parallel structures. This chapter proposes an integrated building block design of a distributed BMS that integrates the power electronics onto battery cells. The new approach brings many advantages including better power utilization, better protection of battery cells, higher equalization speed and efficiency, enhanced reliability and redundancy of the system, and so on. The implementation of the system, design of the integrated module, and the global and local control of the system are studied. The integrated module prototype is designed and experiments are conducted on a three¿¿?module system to verify the concept. View full abstract»

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      MATHEMATICAL MODELING, PERFORMANCE ANALYSIS AND CONTROL OF BATTERY EQUALIZATION SYSTEMS: REVIEW AND RECENT DEVELOPMENTS

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      To reduce the charge imbalance in a battery system, a number of circuit modules are developed and connected to the battery cells to form the battery equalization system. The operation of the battery equalization system is usually controlled by the battery management system. For the design of equalizers, two types of methods have been proposed: passive and active balancing. Of all the structures of battery equalization systems developed, the simplest but widely used one is the series¿¿?connected equalization structure. This chapter discusses the recent research contribution and development about the analysis and control of battery equalization systems. It describes the results on mathematical and computer modeling of battery equalization systems. The chapter reviews the problems of system performance evaluation and control strategies. Once the hardware realization of a new battery equalization topology is developed, experimentation with a prototype is usually carried out to test and demonstrate the performance of the design. View full abstract»

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      REVIEW OF STRUCTURES AND CONTROL OF BATTERY¿¿?SUPERCAPACITOR HYBRID ENERGY STORAGE SYSTEM FOR ELECTRIC VEHICLES

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      The cost and driving performance of electric vehicles (EVs) highly depend on the capability and efficiency of the energy storage system (ESS), which can preserve a large amount of energy, along with the capability of responding instantaneously to the load demand. This chapter reviews the state of the art of battery, supercapacitor, and battery¿¿?supercapacitor hybrid energy storage system (HESS) for advanced EV applications. It discusses the optimal control methods for the HESS and presents the existing battery and supercapacitor technology for automotive applications, respectively. The chapter introduces the control strategy and algorithm for the HESS and summarizes the conclusions and future research directions. The representative characteristic of a passive HESS is the direct combination of the battery and the supercapacitor in parallel. Optimal use of the supercapacitor bank and the battery pack requires an efficient power flow controller between the two energy storage subsystems. View full abstract»

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      POWER MANAGEMENT CONTROL STRATEGY OF BATTERY¿¿?SUPERCAPACITOR HYBRID ENERGY STORAGE SYSTEM USED IN ELECTRIC VEHICLES

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      Energy storage and power management are two critical factors to the performance of various electric vehicles (EV), including pure, hybrid, and plug¿¿?in hybrid EVs. This chapter focuses on the state¿¿?of¿¿?the¿¿?art technologies of hybrid energy storage systems (HESS) for EVs, mainly in power management strategy. It analyzes low¿¿?level hybrid topologies of hybrid system. The chapter discusses high¿¿?level supervisory control between battery and supercapacitor. It describes the advantages and disadvantages of four commonly used hybrid structures for EVs. Effective management of power flow via optimal use of supercapacitor bank and battery pack and via an efficient power flow controller are the key to achieving high performance. The chapter investigates some power management strategies from both time domain control and frequency domain control to improve system efficiency, battery life cycle, and robustness of the hybrid system. A power management strategy based on wavelet transform algorithm has proved effective in dealing with the transient phenomena in load power demand. View full abstract»

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      FEDERAL AND STATE INCENTIVES HEIGHTEN CONSUMER INTEREST IN ELECTRIC VEHICLES

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      Since mid¿¿?1970s, the federal government has provided funding for research into battery and vehicle technologies that could provide an alternative to the gasoline¿¿?powered engine. Federal tax credits for purchasing an electric vehicle, grants and loans to vehicle and parts manufacturers, and greenhouse gas (GHG) regulations have contributed to the evolution of federal support for electric vehicles. Despite major advancements in electric vehicles in the past 40 years, one factor remains elusive: a safe, inexpensive, high energy density battery. The federal role in seeking such a battery technology supplements similar research by domestic (and foreign) automakers. This chapter reviews federal and state efforts to the advance the U.S. electric vehicle and vehicle battery industries. It also reviews government efforts to spur interest in a range of electric vehicles, including hybrid electric vehicles (HEVs), plug¿¿?in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), extended range electric vehicles (EREVs), fuel cell electric vehicles (FCEVs). View full abstract»

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      Index

      Copyright Year: 2017

      Wiley-IEEE Press eBook Chapters

      Addresses the methodology and theoretical foundation of battery manufacturing, service and management systems (BM2S2), and discusses the issues and challenges in these areas

      This book brings together experts in the field to highlight the cutting edge research advances in BM2S2 and to promote an innovative integrated research framework responding to the challenges. There are three major parts included in this book: manufacturing, service, and management. The first part focuses on battery manufacturing systems, including modeling, analysis, design and control, as well as economic and risk analyses.  The second part focuses on information technology’s impact on service systems, such as data-driven reliability modeling, failure prognosis, and service decision making methodologies for battery services. The third part addresses battery management systems (BMS) for control and optimization of battery cells, opera ions, and hybrid storage systems to ensure overall performance and safety, as well as EV management.  The contributors consist of experts from universities, industry research centers, and government agency. In addition, this book:

      • Provides comprehensive overviews of lithium-ion battery and battery electrical vehicle manufacturing, as well as economic returns and government support
      • Introduces integrated models for quality propagation and productivity improvement, as well as indicators for bottleneck identification and mitigation in battery manufacturing
      • Covers models and diagnosis algorithms for battery SOC and SOH estimation, data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH
      • Presents mathematical models and novel structure of battery equalizers in battery management systems (BMS)
      • Reviews the state of the art of battery, supercapacitor, and battery-supercapacitor hybrid energy st rage systems (HESSs) for advanced electric vehicle applications

      Advances in Battery Manufacturing, Services, and Management Systems is written for researchers and engineers working on battery manufacturing, service, operations, logistics, and management. It can also serve as a reference for senior undergraduate and graduate students interested in BM2S2.

      View full abstract»