Tracking and tracing for data mining application in the lithium-ion battery production

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ScienceDirect

Available online at www.sciencedirect.com Available online at www.sciencedirect.com

ScienceDirect

Procedia CIRP 00 (2017) 000–000

www.elsevier.com/locate/procedia

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

28th CIRP Design Conference, May 2018, Nantes, France

A new methodology to analyze the functional and physical architecture of

existing products for an assembly oriented product family identification

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu

Abstract

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

Keywords: Assembly; Design method; Family identification

1. Introduction

Due to the fast development in the domain of communication and an ongoing trend of digitization and digitalization, manufacturing enterprises are facing important challenges in today’s market environments: a continuing tendency towards reduction of product development times and shortened product lifecycles. In addition, there is an increasing demand of customization, being at the same time in a global competition with competitors all over the world. This trend, which is inducing the development from macro to micro markets, results in diminished lot sizes due to augmenting product varieties (high-volume to low-volume production) [1]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing production system, it is important to have a precise knowledge

of the product range and characteristics manufactured and/or assembled in this system. In this context, the main challenge in modelling and analysis is now not only to cope with single products, a limited product range or existing product families, but also to be able to analyze and to compare products to define new product families. It can be observed that classical existing product families are regrouped in function of clients or features. However, assembly oriented product families are hardly to find.

On the product family level, products differ mainly in two main characteristics: (i) the number of components and (ii) the type of components (e.g. mechanical, electrical, electronical).

Classical methodologies considering mainly single products or solitary, already existing product families analyze the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this Procedia CIRP 93 (2020) 162–167

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems 10.1016/j.procir.2020.03.071

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems

53rd CIRP Conference on Manufacturing Systems

ScienceDirect

Procedia CIRP 00 (2019) 000–000

53rd CIRP Conference on Manufacturing Systems

Tracking and Tracing for Data Mining Application in the Lithium-ion

Battery Production

Jacob Wessel

a,b

_{*, Artem Turetskyy}

a,b

_{, Olaf Wojahn}

a,b

_{, Christoph Herrmann}

a,b

_{, Sebastian Thiede}

a,b

a_{Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany} b_{Battery LabFactory Brunaschweig (BLB), Langer Kamp 19, 38106 Braunschweig, Germany} * Corresponding author. Tel.: +49-531-391-7168; fax: +49-531-391-5842. E-mail address: j.wessel@tu-braunschweig.de

Abstract

The production of Lithium-Ion Battery (LIB) cells is characterized by the interlinking of different production processes with a manifold of intermediate products. To be able to ensure high quality and enable a traceability of different production and product characteristics (e.g. energy consumption, material), a tracking and tracing concept is required. In this paper, a practical tracking and tracing concept throughout the production of LIB cells, enabling inline data-driven applications, is introduced. As a part of this concept an intelligent tracking and tracing platform is shown, which allows the generation of a pre-clustered data sets to fa-cilitate future data-driven applications.

Keywords: tracking and tracing; traceability; traceability system, data allocation, data mining; lithium-ion battery cells; manufacturing;

1. Introduction

The raising demand of electric vehicles, new political regulations and trends towards more sustainable mobility concepts are just a few of the factors increasing the pressure on improving the entire life cycle of battery technology sys-tems. The life cycle of a battery cell can be divided into seven different stages: component production (including raw mate-rial production), cell production (electrode manufacturing and cell assembly), module production, pack assembly, inte-gration of battery pack into the final product, battery use phase and end of life (reuse, recycle and remanufacture) [1]. All of these stages embody a manifold of different chal-lenges. The focus in this paper will be on the stage of cell production, which includes the electrode production as well as the cell assembly, as depictured in Figure 1. During the cell production challenges are often driven by the sensitivity of the battery costs to possible production errors or inaccura-cies. This can be explained by the dominance of the material costs and the length of the production chain itself. In recent

years, manufacturing technologies, especially for LIB cells, have improved considerably, but still, due to its high com-plexity, the potential in the manufacturing technology is not yet fully exploited.

Different approaches have been pursued to ensure a ‘higher quality’ or rather ‘higher performance’ (e.g. max. capacity, high energy density) as well as lowering economic and envi-ronmental impacts in the future [1]. Possible solutions based on data-driven methods and applications, have been dis-cussed in several works [2 - 4]. Those methods strive towards uncovering unknown interrelations or interdependencies in manufacturing chain on the basis of manufacturing data. All of these methods require highly diverse data, to be able to be Figure 1: Classification of this publication in the life stage of LIB

Raw Material _ProductionElectrode _AssemblyCell _AssemblyModule Use Phase End-of-Life

Focus of this Paper

Allocation Potential use in later stages (traceability)

Available online at www.sciencedirect.com

ScienceDirect

Procedia CIRP 00 (2019) 000–000

53rd CIRP Conference on Manufacturing Systems

Tracking and Tracing for Data Mining Application in the Lithium-ion

Battery Production

Jacob Wessel

a,b

_{*, Artem Turetskyy}

a,b

_{, Olaf Wojahn}

a,b

_{, Christoph Herrmann}

a,b

_{, Sebastian Thiede}

a,b

a_{Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany} b_{Battery LabFactory Brunaschweig (BLB), Langer Kamp 19, 38106 Braunschweig, Germany} * Corresponding author. Tel.: +49-531-391-7168; fax: +49-531-391-5842. E-mail address: j.wessel@tu-braunschweig.de

Abstract

The production of Lithium-Ion Battery (LIB) cells is characterized by the interlinking of different production processes with a manifold of intermediate products. To be able to ensure high quality and enable a traceability of different production and product characteristics (e.g. energy consumption, material), a tracking and tracing concept is required. In this paper, a practical tracking and tracing concept throughout the production of LIB cells, enabling inline data-driven applications, is introduced. As a part of this concept an intelligent tracking and tracing platform is shown, which allows the generation of a pre-clustered data sets to fa-cilitate future data-driven applications.

Keywords: tracking and tracing; traceability; traceability system, data allocation, data mining; lithium-ion battery cells; manufacturing;

1. Introduction

The raising demand of electric vehicles, new political regulations and trends towards more sustainable mobility concepts are just a few of the factors increasing the pressure on improving the entire life cycle of battery technology sys-tems. The life cycle of a battery cell can be divided into seven different stages: component production (including raw mate-rial production), cell production (electrode manufacturing and cell assembly), module production, pack assembly, inte-gration of battery pack into the final product, battery use phase and end of life (reuse, recycle and remanufacture) [1]. All of these stages embody a manifold of different chal-lenges. The focus in this paper will be on the stage of cell production, which includes the electrode production as well as the cell assembly, as depictured in Figure 1. During the cell production challenges are often driven by the sensitivity of the battery costs to possible production errors or inaccura-cies. This can be explained by the dominance of the material costs and the length of the production chain itself. In recent

years, manufacturing technologies, especially for LIB cells, have improved considerably, but still, due to its high com-plexity, the potential in the manufacturing technology is not yet fully exploited.

Different approaches have been pursued to ensure a ‘higher quality’ or rather ‘higher performance’ (e.g. max. capacity, high energy density) as well as lowering economic and envi-ronmental impacts in the future [1]. Possible solutions based on data-driven methods and applications, have been dis-cussed in several works [2 - 4]. Those methods strive towards uncovering unknown interrelations or interdependencies in manufacturing chain on the basis of manufacturing data. All of these methods require highly diverse data, to be able to be Figure 1: Classification of this publication in the life stage of LIB

Raw Material _ProductionElectrode _AssemblyCell _AssemblyModule Use Phase End-of-Life

Focus of this Paper

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used to their fullest potential. The acquired manufacturing data sets are often not consistent, which is an ongoing chal-lenge not only in all manufacturing environments. Espe-cially, the combination of continuous and discrete manufac-turing processes in LIB production amplifies this issue. To facilitate data-driven approaches, tracking and tracing of im-portant materials, intermediate product characteristics and all related process data throughout the entire manufacturing chain becomes more and more important [2]. Furthermore, with more research focusing on developments towards ena-bling smart factories of the future, traceability data manage-ment in complex-process environmanage-ments is a key factor to achieve a competitive edge in diverse areas such as optimi-zation, product quality, and error proofing [5].

To enable such aforementioned data-driven methods, an ex-plicit traceability of all products, intermediate products and all production and product characteristics in the manufactur-ing of LIB cells (e.g. process parameters, energy consump-tion, material) is required. A total traceability can be seen as an enabler for a better decision making process, where trace-ability data ensures a more consistent or higher quality data, as depictured in Figure 2. This paper will focus on the devel-opment of a methodology for the design of a traceability sys-tem for the manufacturing of LIB Cells. First, an introduction into the term of traceability and traceability systems with its elements and a summary of the most well-known state-of-the-art technologies and industry solutions is given. In chap-ter 3 a methodology for designing a traceability system is presented, which will is applied to the LIB cell manufactur-ing of the Battery LabFactory Braunschweig (BLB) in chap-ter 4.

2. Literature Review: Traceability and Traceability System

2.1 Definition of Traceability

The term of traceability occurs in many different research fields. This is one reason why there are many different mean-ings and interpretations of the term. In this paper, as well as in many research fields, traceability is defined as an essential subsystem of quality management. The ISO 9000 2015 Qual-ity management systems – Fundamentals and vocabulary de-fines the term as “… the ability to trace the history,

applica-tion or locaapplica-tion of an object” [6]. Here an object is described

as anything that is perceivable or conceivable. Traceability or tracking and tracing can be broken down into two core elements: (1) trace and (2) track. With the ability to trace an object the extraction of information about the object (or trace-object) is enabled [7]. This can be described as a pro-cess to recreate the composition (e.g. product history) as well as its value generation (process history) of an object in form of an interlinked and consistent data set [8]. For this, infor-mation about the traced-object has to be recorded and saved,

in other words tracked, downstream its life cycle. Further-more, Traceability often occurs in two different types.

inter-nal traceability, which describes the tracing of an object with

its corresponding information inside a company and chain

traceability, where links between the exchanging objects in

a supply chain are established (back vs. one-step-forward traceability). In a chain traceability system, the in-formation about the product is either stored locally while only sending the objects identification along or the infor-mation follows the object throughout the entire supply chain [9].

2.2. Elements and Function of Traceability

As mentioned above traceability consists of two core el-ements: (1) the tracking of an object with its corresponding information and (2) the tracing of this object throughout its life cycle or during certain stages of its life cycle [9]. With the enabled capability of tracking a specific object, its down-stream path, based on certain defined points in the life cycle, can be followed. Essential for tracking an object is a unique identification (ID) or authentication. This ID can serve as a key to extract the digital identity (or traceability-data) from a data source or ID-container. There are several possibilities for classification of a unique identification or authentication of the trace-object. The identification can be performed by knowledge (“something they know”), by ownership (“some-thing they have”) or by characteristic feature (“some(“some-thing they are”) [10]. With the second traceability element, the tracing, the capability to identify the origins and characteris-tics of the product when following its upstream path in a life cycle or stages of its life cycle, is reconstructed. This process again is based on data recorded at defined points during an object’s life cycle [11]. There are several technologies used when tracing an object and thereby extracting information with its unique ID. According to Liukkonen et al. those tech-nologies can be classified into those where the item carriers the identifier and those where the identification is carried by humans [12]. Some exemplary identification technologies are illustrated in Figure 3. In the manufacturing environment the most commonly used identification technologies are ob-ject ID carrier using authentication by ownership, since a physical object can only indirectly provide knowledge to au-thenticate its identity. Object carrier ID mechanisms can be distinguished in three main levels of identification: (1) class-level identification (an object is identifiable by its product or part ID), (2) batch/lot-level identification (product or part ID is extended with a batch/lot number), (3) instance-level iden-tification (object is identified with a serialized ID). Authen-tication by ownership has widely been implanted and mostly relies on Automatic Identification (Auto-ID) technologies (e.g. barcode, RFID). There are several studies presenting the usage of RFID-technologies in manufacturing environment [13 - 15]. Recent studies also introduce the use of blockchain technology to facilitate chain traceability. Kim et al. presents an ontology-driven methodology proving the provenance (origin or location) of a physical good [16]. Furthermore,

Tracking and Tracing Data analytics

Present Past monitoring, intervention Future prediction, prevention Figure 2: Tracking and Tracing as an enabler for Data Analytics. [11]

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Kuhn et al. developed a blockchain enabled traceability ap-proach across companies and country boarders for electrical system components [8]. In their view, blockchain solutions can provide continuous data sets free from fraud, which can be used to improve quality, failure prevention and predic-tions. One downside of those technologies is the required computational power to support blockchain technologies.

2.3 Tracking and tracing system

When aiming towards a unique traceability of the life cy-cle of an object or of certain stages of its life cycy-cle, a tracking and tracing system needs to be established. A tracking and tracing system, or traceability system, can be explained as a series of mechanisms, procedures and routines to manage traceability of the life cycle of an object. In the food industry functions performed within a traceability system cover the (1) identification, (2) linkage, (3) information recording, (4) information storing and sharing and (5) verification of the system [11], [17]. Those functions can also be applied when constructing a traceability system in the manufacturing in-dustry. One essential definition for every traceability system is the definition of a Traceable Resource Unit (TRU). A TRU is easy to be define for a batch process, since no other unit or object can have the same or comparable characteristics. De-fining a TRU for continuous processes is more difficult, but mostly relates e.g. to the used raw material or the change in processing conditions of the object [16]. According to Karlsen et al. there are only three different types of traceable unit: batch or product, trade unit and logistic unit (e.g. pallet, container etc.) [7]. Since most organization extend over dif-ferent departments and working units, when implementing a traceability system, a standardizations are critical. Two of the most important definition are: (1) the definition of Critical Traceability Points along the life cycle (CTPs) and (2) the definition of Key Data Elements (KDEs). A CTP is a place in the life cycle or supply or manufacturing chain, where sys-tematic loss of information about the product, process or ser-vice occurs [7]. The KDEs carry information describing the actual instance of the CTPs and cover five important dimen-sions: Who, What, Where, When and Why. Those data points facilitate the later identification and interlinkage of further information or data concerning the trace-object. Possible fur-ther information can e.g. be total material used, energy de-mand during its production, quality criteria or predicted per-formance of the intermediate or final product.

2.4. Exemplary traceability systems in industry

There are several tracking and tracing solutions deployed across different industry sectors. The Metro Group imple-mented the first RFID Tracking and Tracing system across its entire supply chain (production to supermarket) [18]. With this system in place a transparent depiction of material flows, allows stock replenishment to take place via auto-mated triggers by the management system. More recently, IBM and Maersk deployed their first industry-wide cross-border supply chain solution based on blockchain technol-ogy. This solution is meant to digitize the supply chain pro-cess from end-to-end to globally enhance transparency and a secure information sharing among trading partners [19]. There is a high variety of specialized solutions available, e.g. Optel’s GeoTraceability technology is designed to track the authenticity of raw materials. Furthermore, with many new regulations e.g. in the pharmaceutics sector to enforce supply chain security and traceability, solutions aiming towards eliminating counterfeit and enabling supply chain transpar-ency are booming. The network TraceLink is world largest track-and-trace network. With their software, based on Am-azon Web Services AWS, manufacturers, distributors and dispensers can secure their drug supply chain and stop the trade of counterfeit medicines. With the demand of LIB dou-bling roughly every five years, tracing their provenience, es-pecially the origin of the raw materials (e.g. rare earths) to improve the sourcing is critical. Recently, Volvo announced the development of a blockchain-based technology proving the origin of cobalt used in their EVs. The data carried by the blockchain will include the cobalt’s origin, information about weight and size and the chain custody to enable its chain transparency [20]. Nevertheless, not only tracing their provenance of the material is of importance, Northvolt em-phasis in their outlook on the future state of the art for battery industry the importance of a tracking and tracing system, which allocates data down to a single cell instead of batch-level. This is a requirement to be able to better understand an extract knowledge from all data gathered along the manufac-turing chain to improve processes [21]. This conclusion is also made within the Road Map 2030 of Battery Production by the VDMA, which outlines the potential benefits of a tracking and tracing system to enable the traceability of in-termediate products [22].

Object ID carrier Human ID carrier

Shape

recognition recognitionMagnetic Optical tags Electronic tags Physimetric ID Knowledge-based Biometrics

Mechanical

keys Magnetic stripes Barcode (1D, 2D) ORC Optical memories Bokodes RFID NFC Smart chips Artificial features Natural features Finger print Facial recognition Voice recognition DNA Network ID Telephone number IPv6 MAC … Authentication by Knowledge Authentication

by Ownership by Characteristic featuresAuthentication

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2.5. Conclusion and need for action

Considering different industry solutions enabling the traceability of an object, many differences in e.g. what to track and how to track and trace an object, can be identified. This observation was also made by Olsen et al. in his paper on “How to define traceability” [23]. His work outlines the different interpretations and definitions of the term traceabil-ity, which can lead to inconsistencies and confusion when designing a traceability system. The need for clear standards or a generic methodology is still present today.

3. Concept for the designing of a traceability system To meet the identified challenge of developing a func-tional traceability system a generic methodology according to the process is presented in this paper. The PDCA-Process (Plan-Do-Check-Act) is structured in four phases: (1) situation analysis, (2) system design, (3) introduction and prototyping phase of the system and (4) validation and pos-sible adaption of the system. This work focuses on the system

design phase. To better understand what different features or

functions are required when developing a specific traceabil-ity system, a morphological analysis is performed. Through this analysis the complex problem of how to design a func-tional traceability system is decomposed. This system is de-fined within the situation analysis, into different functions and principles in a morphological box, as depictured in Fig-ure 4. A morphological box is a tool to generate possible so-lutions for complex problems, by breaking the problem down into its essential parameters or dimensions (here: functions and principles of a traceability system, as defined in chapter 2.3) into a multi-dimensional matrix [24]. For each parame-ter or dimension, solutions or features are identified. All pos-sible combinations of those features represent pospos-sible solu-tions to the problem at hand. In the case of the design of a traceability system, different principles for the four of the five functions of a traceability system are defined: (I) identi-fication of product and information Flow, (II) linkage of the

TRU and ID, (III) information recording and (IV) infor-mation storage and sharing. To further decompose the possi-ble solution space, features or solutions to meet principles A to K were identified and depictured in the morphological box. Those feature can e.g. be possible solution, methods or characteristics of the feature. By combining features and so-lutions of different principles a paths identifying a possible solution is created. With a morphological box a high variety of different solutions solving the same issue can be identi-fied. Here, in the context of designing a traceability system, the solution paths with their selected features delimit the pos-sible solution space, by what confusion when designing the system is reduced. The dashed line in Figure 4, represents the solution space for the design of a specific traceability system in the BLB, which will be explained in the next chapter. 4. Energy demand based traceability system for battery cell production

The concept presented above was applied for the devel-opment of a tracking and tracing concept for the pilot lith-ium-ion battery cell manufacturing of the BLB. The BLB op-erates a highly flexible production line that allows research and small-scale production. The development process was aligned with the above suggested PDCA-process in the phases (1) situation analysis, (2) system design, (3) introduc-tion and prototyping phase and (4) system validaintroduc-tion.

4.1. Situation Analysis: Definition of System Scope

The system boundaries were set to be within the produc-tion facility. This includes the entire manufacturing chain from mixing of the slurry to the formation and aging of the LIB cell. The manufacturing chain can be subdivided into three phases, i.e. electrode production, covering mixing, coating, drying, and calendering, cell assembly, covering the cutting of the electrodes, stacking/folding, housing, contact-ing and the electrolyte fillcontact-ing, and formation and agcontact-ing. Those processes cover batch or semi-continuous processes as

Figure 4: Morphological box for the design of a traceability system.

Plan – Situation Analysis:  Definition of Objectives and Aims

 Scope of System, … Do – System Design

Check – Introduction and Prototype phase:  Process manual, training of relevant personal, …

Act – Validation and Adaption:  Monitoring, Audit, …  Improving or renewing the system

(I) Identification of Product and Information Flow

(A) Traceable Resource Unit (B) ID Allocation (C) Method ID Attaching (D) Segregation Management

(II) Linkage of TRU and ID

(E) Media for Information Recording (F) Transmission Media Identification (III) Information Recording (G) Information Accuracy (H) Information Type (IV) Information Storage and Sharing

(I) Data Management System (J) Data Accessibility (K) Data Security

Raw material Single Unit Batch Processing _conditions …

By Knowledge By Ownership By Characteristics …

Object ID carrier Human ID carrier …

Physical entity Unit of time …

Magnetic recognition Optical tags Electronic tags Network ID …

Paper forms Electronic Database Labelling Electronic Tags …

Operational Tactical Strategic …

int float bool list …

Decentralized On-Premise Cloud …

Open Access Registered Assess Controlled Access …

Physical Level Host/ Virtual _Level Interface Level _{System Level}Operational Database Level Application _Level

Principles

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well as single unit production. The manufacturing chain is depicted in the top part of Figure 5 A. Each process trans-forms an initial product (e.g. raw material) into an interme-diate product or the final product with certain (intermeinterme-diate) product features. Since the data acquisition concept of the entire manufacturing process at the BLB with all relevant data sources was presented in [3] the tracking and tracing system is built upon this legacy. The aim of the traceability system for the BLB is to facilitate inline data-driven methods by generating a pre-clustered and consistent data set. An im-portant objective was capturing, storing and managing infor-mation automatically, in order to deploy a successful tracea-bility system by shifting from error-prone mostly manual processes to automated processes. Since, every process step results in a new intermediate product with new features, every processing step was defined as a CTP, where key data elements (e.g. start and end of processing, operating ma-chine) had to be tracked to enable the reconstruction of the product or intermediate product history.

4.2. System Design: Traceability System

For the design of the traceability system, three levels were considered: (1) a data acquisition and management (2) a method of data preparation and (3) a procedure and concept for the data access. All collected data through data acquisi-tion and management system of the BLB is either automated-acquired (in-line) or manual-automated-acquired (off-line) data, which is stored in a data storage on-premise, in a form on a Data Warehouse. The energy demand data of each machine along the manufacturing chain is tracked through an Energy De-mand Monitoring (EDM) and saved in a local data base. The EDM detects different states in the power load data of each machine through defined thresholds for four different states: (1) machine is turned off, (2) machine is in standby, (3) ramp up of the machine or (4) machine is in production. The Track-ing & TracTrack-ing Supervision System (TaTSS), depicted in the center of Figure 5 A, hovers above this EDM monitoring all energy data, generating an empty information container with

a specific ID automatically whenever a machine is in the state of “production”. This process stage identification is shown in Figure 5 B with exemplary energy load data. In this case the TaTSS detected a machine in the production state for 43 min. The timestamp for this production is also detected and passed along to the specific ID. The specific ID of each information container, generated by the TaTSS, hereby em-bodies information for the two of the five traceability dimen-sions Where and When. By knowing the time, duration and location, the information container retrieves all accumulating data, i.e. sensor data, control data, energy demand, to this specific location or process step. The missing three dimen-sions, What, Who and Why, to allow a total traceability of the object created are provided via Tracking & Tracing level, which is depicted in the lower part of Figure 5 A. This allows the extraction of production and product specific data in a form of a virtual product information card.

4.3. Introduction and Prototype phase

The developed system was introduced at the BLB. The TaT-System was enabled through a web-based platform, which is accessible as a registered user at each CTP via a mobile device or from a local computer. Here, information, about what product was produced by whom for what reason, is tracked. It is than associated to the same specific ID gen-erated by the TaTSS. By defining the dimension what in the TaT-System or in other words, linking the production with the produced or processed (intermediate) product, the infor-mation container of the process and its resulting product are interlinked. This achieved through a developed back-end of relational databases each referring to the specific production ID. This allows the Tracking and Tracing system to work for the highly complex production system of LIB and to auto-matically acquire all product-specific off-line data, such as quality parameters of the inherited intermediate products, material data or batch number of the raw material. As the manufacturing chain of LIB cells is divided into continu-ous/batch and single unit production steps, two TRUs are to

Figure 5: The developed Tracking and Tracing System at the BLB with its different levels.

Mixing Coating / Drying Calendering Cutting Assembly Housing Contacting Filling Forming / Ageing

Data Storage

Energy Demand Monitoring

Virtual Product Information Card

Batch production Single Unit

Mixing EM PLC Coating / Drying EM PLC Calendering EM PLC Cutting EM PLC Forming / Ageing EM PLC Filling EM PLC Contacting EM PLC Housing EM PLC Assembly EM PLC

Tracking & Tracing Supervision System - TaTSS Automated assignment of acquired data to production relevant process

Tracking & Tracing - TaT Enable tracing of ID relevant information

Automated Production Data Acquisition

Sensor Sensor Sensor Sensor Sensor Sensor Sensor Sensor Sensor

III. Da ta Ac cess II. D ata Pr ep ar ati on I. D ata Ac qui siti on a nd M ana ge m ent Where? When? Who? What? Why?

B. Detected Production Stage through Energy Monitoring.

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be defined. The first TRU relates to the processing of raw material during the mixing process and is set to the time and location during its further processing. This continues throughout the electrode production, which allow the alloca-tion of all acquired data to a specific place on the electrode coil. With the start of the cell assembly the production changes into a single unit production steps. Here, not only the resulting cell stack is tracked and defined to be the TRU but also the processed electrode sections for each cell stack are tracked. This can be enabled through a continuous mark-ing of the electrode durmark-ing coatmark-ing process. With each spe-cific product and production ID all relevant virtual product information is made accessible for in-line data-driven appli-cations. Furthermore, each worker in the BLB can access in-formation about the current status of the production or the perceived quality of the (intermediate) product to improve the transparency inside the whole organization.

4.4. System Validation

To verify the total traceability of all material and energy flow in the BLB, a function to monitor all entries and identi-fied production was included to the TaTSS. Through this sys-tem, unassigned intermediate products and machines in the state of production are detected. When this system detects unassigned entries an automated reminder is sent via E-Mail to the process operator. With this, manual processes are su-pervised to eliminate possible errors.

5. Conclusion and Outlook

A methodology for the design of a tracking and tracing system based on a morphological analysis is presented. This approach was applied within the BLB for the entire manu-facturing chain of LIB cells to enable inline data-driven ap-plications. The developed system enables the automated identification of processes and linking of all acquired prod-uct-specific data. However, the deployment of the traceabil-ity data in data-driven applications has not been performed yet. Further work will focus on unique identification of elec-trodes inside a specific cell and the deployment of data-driven application on the basis of the allocated traceability data.

Acknowledgements

The authors thank the BMWI – Federal Ministry of Eco-nomic Affairs and Energy for supporting the project DaLion 4.0 (03ETE017A) and the collaborators within the project. References

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