Jeffrey A. Abell

Joining technologies for automotive lithium-ion battery manufacturing - A review

Automotive battery packs for electric vehicles (EV), hybrid electric vehicles (HEV), and plug-in hybrid electric vehicles (PHEV) typically consist of a large number of battery cells. These cells must be assembled together with robust mechanical and electrical joints. Joining of battery cells presents several challenges such as welding of highly conductive and dissimilar materials, multiple sheets joining, and varying material thickness combinations. In addition, different cell types and pack configurations have implications for battery joining methods. This paper provides a comprehensive review of joining technologies and processes for automotive lithium-ion battery manufacturing. It details the advantages and disadvantages of the joining technologies as related to battery manufacturing, including resistance welding, laser welding, ultrasonic welding and mechanical joining, and discusses corresponding manufacturing issues. Joining processes for electrode-to-tab, tab-to-tab (tab-to-bus bar), and module-to-module assembly are discussed with respect to cell types and pack configuration.Copyright

Transient Temperature and Heat Flux Measurement in Ultrasonic Joining of Battery Tabs Using Thin-Film Microsensors

Procеss physіcs undеrstаndіng, rеаl tіmе monіtorіng аnd control of vаrіous mаnufаcturіng processes, such аs bаttеry mаnufаcturіng, аrе crucіаl for product quаlіty аssurаncе. Whіlе ultrаsonіc wеldіng hаs bееn usеd for joining bаttеries in еlеctrіc vеhіclеs, the welding physics and process attributes, such as the heat generation and heat flow during the joining process, іs stіll not wеll understood lеаdіng to tіmе-consumіng trіаl-аnd-еrror bаsеd procеss optіmіzаtіon. Thіs study іs to іnvеstіgаtе thеrmаl phеnomеnа (і.е. transient tеmpеrаturе аnd hеаt flux) by using mіcro thіn fіlm thеrmocouplе (TFTC) аnd thіn fіlm thеrmopіlе (TFTP) аrrаys (referred to as micro sensors in this report) аt thе vеry vіcіnіty of thе ultrаsonіc wеldіng spot. Micro sеnsors were first fаbrіcаtеd on the buss bаrs. A series of experiments were then conducted to investigate the dynamic heat generation during the welding process. Expеrіmеntаl rеsults showеd that TFTCs еnаblеd thе sеnsіng of trаnsіеnt tеmpеrаturеs wіth much hіghеr spаtіаl аnd tеmporаl rеsolutіons thаn convеntіonаl thеrmocouplеs. It was further found that the TFTPs were more sensitive to thе trаnsіеnt heat generation process during welding thаn TFTCs. Morе sіgnіfіcаntly, the hеаt flux chаngе rаtе was found to be able to provіdе better іnsіght for the process. It provided evidence indicating thаt thе ultrаsonіc welding procеss іnvolvеs thrее dіstіnct stаgеs, і.е., frіctіon hеаtіng, plаstіc work аnd dіffusіon bondіng stаgеs. Thе hеаt flux chаngе rаtе thus hаs sіgnіfіcаnt potеntіаl to identify the in-situ welding quality, in the context of welding procеss monіtorіng аnd control of ultrаsonіc wеldіng procеss. The weld samples were examed using scanning electron microscopy (SEM) and energy dispersive X-ray spectropy (EDS) to study the material interactions at the bonding interface as a function of weld time, and have successfully validated the proposed three-stage welding theory. As a case study, TFTCs were fabricated on Si wafers for sensor insertion units, which were successfully inserted into a pre-machined slot in the welding anvil as a temperature sensing device. Dynamic temperature rises during welding were successfully measured with excellent repeatability.

Analysis of Weld Formation in Multilayer Ultrasonic Metal Welding Using High-Speed Images

One of the major challenges in manufacturing automotive lithium-ion batteries and battery packs is to achieve consistent weld quality in joining multiple layers of dissimilar materials. While most fusion welding processes face difficulties in such joining, ultrasonic welding stands out as the ideal method. However, inconsistency of weld quality still exists because of limited knowledge on the weld formation through the multiple interfaces. This study aims to establish real-time phenomenological observation on the multilayer ultrasonic welding process by analyzing the vibration behavior of metal layers. Such behavior is characterized by a direct measurement of the lateral displacement of each metal layer using high-speed images. Two different weld tools are used in order to investigate the effect of tool geometry on the weld formation mechanism and the overall joint quality. A series of microscopies and bond density measurements is carried out to validate the observations and hypotheses of those phenomena in multilayer ultrasonic welding. The results of this study enhance the understanding of the ultrasonic welding process of multiple metal sheets and provide insights for optimum tool design to improve the quality of multilayer joints.

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and postweld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.

Distributed System Development Risk Analysis

Under the current trend of globalization, companies develop products not only to target a single market but to sell them to the entire world. Companies realize the importance of collaborative design, or workshare, to develop global regional engineering centers to balance design variations while ensuring local market success. This paradigm shift enables diverse customer values to be integrated into products but also introduces challenges in the management of work distribution. Extensive industry case studies have shown that capability and capacity of the regional centers drive the workshare decisions; however, this strategy overlooks the interdependence of the design systems causing many delays and quality problems. Seeing the opportunity, this paper presents a method to identify and to quantify the system-level workshare risk based on the couplings of system components to evaluate the overall workshare scenarios. The risk analysis consists of two key elements in terms of two relationships, the division of work for distributions (i.e., component coupling) and the work assignments of the distributed teams (i.e., workshare coupling), as well as an algorithm to combine the relationships to calculate the workshare risk. To illustrate the steps of the risk analysis, this paper applies it to a hair dryer design as a case study. The paper also discusses the usages and characteristics of the risk analysis, and concludes with the future research and the next steps of generalizing the method to other product development projects.

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a monitoring algorithm using a quadratic classifier and features that are extracted from space and frequency domains of cross-sectional profiles on tool surfaces. The developed algorithm is validated using tool measurement data from a battery plant. [DOI: 10.1115/1.4031677]

Benchmarking of high capacity battery module/pack design for automatic assembly system

Electric vehicles (EV), including plug-in hybrid and extend-range EVs, rely on high power and high capacity batteries, such as lithium-ion batteries, as the main source of propulsion energy. The EV battery technology is progressing rapidly as a plurality of battery designs in cells, modules and packs are emerging on the market. Current EV battery pack assembly is mostly manual and has faced significant challenges in coping with such fast development of automotive batteries. Meanwhile, there is a lack of systematic study on the implications of varieties in battery designs on assembly system. This paper reviews various battery module or pack designs and characterizes them from the assembly process perspective, and discusses their implications with respect to assembly methods, process flexibility and automation feasibility. The associated cost, quality, and safety issues of assembly are also addressed and research opportunities and innovations are discussed. This study can assist in creating guidelines on the development of new generations of battery products that enables highly efficient and responsive battery assembly.Copyright

Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy

Discussion of big data has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that big data has changed problem solving: the problems we choose to solve, the strategy we seek, and the tools we employ. This paper illustrates this point by showing how the big data style of thinking enabled the development of a new quality assurance philosophy called process monitoring for quality (PMQ). PMQ is a blend of process monitoring and quality control that is founded on big data and big model, which are catalysts for the next step in the evolution of the quality movement. Process monitoring for quality was used to evaluate the performance of the ultrasonically welded battery tabs in the new Chevrolet Volt, an extended range electric vehicle. Index terms — Manufacturing, big data, big models, problem solving strategy, process monitoring for quality, acsensorization, quality control Nomenclature α rate of type I error β rate of type II error BD big data BDBM big data – big models BM big models D3M data-drive discovery of models DFSS design for six sigma DLL dynamic-link library GP genetic programming ICE internal combustion engine LVDT linear variable differential transformer MCS multiple classifier systems MPCD maximum probability of correct decision PMQ process monitoring for quality

Sensitivity Analysis on Flexray Dynamic Segment Design Parameters

The Flexray protocol is rapidly emerging as the next generation in-vehicle communication protocol that will accommodate safety critical, high bandwidth and real time requirements coming from the burgeoning increase of automotive electronic content. The Flexray protocol provides a globally synchronized time base and has provisions for a Time Division Multiple Access (TDMA) based transmit model (static segment) and a priority based transmit model (dynamic segment). The challenging aspect about Flexray is leveraging its degree of flexibility. Just configuring the dynamic segment alone involves ten protocol level parameters, each of which has significant affect on various key performance metrics such as the message transmission latency, number of message overwrites and bus utilization. This paper presents results from sensitivity analysis for these key parameters relative to the various performance metrics. The results seem to indicate that the assumed notion of message priority as being the main driver that affects transmission latency is far from accurate. Also, the results clearly indicate that one of the in-built protocol constraints has a major detrimental effect on the performance metrics. This paper proposes the removal of this constraint and indicates the degree of performance improvement that can be achieved without the constraint. Finally, a brief discussion on the conclusions of this study and future work will be provided.

Characterization of ultrasonic metal welding by correlating online sensor signals with weld attributes

Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and post-weld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.Copyright