PLANT MANAGEMENT
it is necessary to handle delicate, thin foil materials at high speed without sacrificing accuracy and precision. As battery energy density needs to continue to increase, the foils used may even become thinner, depending on the chosen technology, and thus more difficult to process. Tis means that producers need to find solid solutions for optimal tensioning. Secondly, inaccuracies and impurities accumulate through the different processing stages and conventional quality control may lead to high reject rates. Tese aspects can ultimately affect productivity and efficiency. In addition to these issues, LIB cell
producers should consider other key aspects. For example, with the metals required by the industry being scarce, expensive and involving carbon-intensive mining processes, it is crucial for manufacturers to ensure optimum resource use for efficient LIB production. By doing this, they can minimise their costs and environmental impact while increasing their profitability and ROI. Addressing the potential of any
battery performance issues is another challenge that manufacturers should be proactive about. Being able to leverage material track and trace systems to quickly complete root cause analysis and address any processing issues on the shop floor is key to avoiding the release of sub-par products.
LEVERAGING THE POWER OF BIG DATA Data-driven, quality assurance strategies are well suited to supporting companies
in developing optimised production environments. By promptly flagging anomalies, detecting and preventing quality shortcomings, especially in the early process steps, they help avoid larger issues further down the production line. Tis, in turn, can reduce the volume of off-spec and sub- optimal LIB cells while improving material and energy usage. Besides, data-driven approaches are at the core of track and trace strategies, as they can provide accurate insights, even in continuous processes, which are highly complex. Companies should therefore invest in automation devices that can generate key data on processes, equipment, and materials being used. Tese should then be analysed or mined to create predictive models that can be used to identify the parameters that determine product quality, optimum operations, and asset performance. Te data-crunching power of today’s technology based on artificial intelligence (AI) is unprecedented. It is so advanced that traditional expert-based methods cannot even compete. In fact, they can scan through large volumes of figures, identify key patterns, inliners and outliners as well as interpolating multiple datasets to generate predictive algorithms on different processes. Besides, it is possible to develop overarching models that take all the different steps and assets of the whole LIB cell production chain into account to provide key indications on final product quality. Once this knowledge is generated, LIB companies can leverage these insights to monitor and control their activities in real- time. Even more, by using time stamps
and marks for film section identification, they can facilitate tracking and tracing that ultimately aid serialisation, problem-solving and root cause identification. Tanks to all these aspects, data-driven LIB manufacturers can benefit from considerable savings in terms of efficiency and costs, which can drive profitability and sustainability. For example, a non-data- oriented 10GWh production line may be producing 70%,10%, 5%, and 5% of cells that offer, respectively, 100%, 90%, 80% and 70% capacity with 10% of products that need to be scrapped. Considering the overall costs associated with each single cell, if smart manufacturing can improve these capabilities even by a single percentage point, the environmental and financial impact can be considerable. In effect, each 1% scrap avoided in a 10GWh line that generates revenue of €90/kWh of battery capacity results in €9 million of savings. Tere are more opportunities to
consider, as the adoption of smart, data- driven LIB cell factories lead to continually ongoing optimisation of manufacturing processes. Te predictive models developed can utilise new data, which is regularly generated by automated equipment, to refine their forecasts and provide ever more accurate insights to improve operations, products, and assets. As a result, manufacturers can use these as part of continuous improvement strategies that can help strengthen competitiveness in the long run.
At the core of EVs are LIB packs, where a number of cells are assembled in a frame to form a module
Companies interested in advancing their LIB cell production lines can now leverage a number of proven, advanced solutions. A prime example is offered by Mitsubishi Electric, which launched an innovative line scan bar to inspect the surface of in-process materials in real-time. Te instrument is equipped with contact image sensor (CIS) technology, to provide high resolution feedback on the surface conditions of the coated current collector. By processing the data generated by this, companies can leverage a key tool to help determine the quality of LIB cells with extremely high resolution and accuracy, ultimately improving end-product quality.
Klaus Petersen is with Mitsubishi Electric Europe.
www.mitsubishielectric.com
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