A scalable life cycle inventory of an automotive power electronic inverter unit\u2014part II: manufacturing processes

Anders Nordelöf

doi:10.1007/s11367-018-1491-3

Abstract

PurposeA scalable life cycle inventory (LCI) model, which provides mass composition and gate-to-gate manufacturing data for a power electronic inverter unit intended for controlling electric vehicle propulsion motors, was developed. The purpose is to fill existing data gaps for life cycle assessment (LCA) of electric vehicles. The model comprises new and easy-to-use data with sufficient level of detail to enable proper component scaling and in-depth analysis of inverter units. The aim of this article (part II) is to describe the modeling of all production steps and present new datasets. Another objective is to explain the strategies for data collection, system boundaries, and how unit process datasets were made to interact properly with the scalable design model (part I).MethodsData for the manufacturing of the inverter unit was collected from a variety of literature, technical specifications, factory data, site visits, and expert interviews. The model represents current levels of technology and modern industrial scale production. Industry data dates back to 2012. Some older literature is referred to, but only if it was found to remain relevant. Upstream, new data has been gathered to the point where the Ecoinvent database can be used to model a full cradle-to-gate inventory. To make the LCI model easy to use, each flow crossing the system boundary is reported with a recommended linked flow to this database.Results and discussionThe screening and modeling of manufacturing inverter units resulted in a substantial compilation of new inventory data. In close integration with the design model, which is scalable in size over a range of 20–200 kW in nominal power and 250–700 V in DC system voltage (part I), it forms a comprehensive scalable LCI model of a typical automotive power electronic inverter unit intended for traction motor control. New production data covers electroplating of gold, electro-galvanization, machining and anodizing of aluminum, ceramic substrate fabrication, direct copper bonding, photoimaging and regenerative etching, power module assembly with a two-step soldering process, and the assembly of automotive printed circuit boards.ConclusionsInterviews with experts were found to be vital for effective data collection and the reporting of details a key to maintaining data usability over time, for reuse, rework, and criticism by other LCA practitioners.

Highlights

1.1 BackgroundThe production of active electronic semiconductor devices, for example, power transistor chips, demands large amounts of chemicals and energy per unit of mass compared to other products (Williams et al 2002)
Responsible editor: Zbigniew Stanislaw Klos. Preamble This is the second article in a series of two presenting a new scalable life cycle inventory (LCI) data model of a power electronic inverter unit for control of electrical machines in vehicles, available to download
In part II, it is described how new production datasets were compiled from literature and factory data to cover the manufacturing chain of all parts, including the power module fabrication, mounting of printed circuit boards and the complete assembly

Summary

Introduction

1.1 BackgroundThe production of active electronic semiconductor devices, for example, power transistor chips, demands large amounts of chemicals and energy per unit of mass compared to other products (Williams et al 2002). The fabrication and assembly of printed circuit boards (PCBs), as well as the modules for packaging power semiconductors, require a variety of materials, process chemicals, and furnace heat treatments (Sheng and Colino 2005; Coombs 2008), with related environmental impacts. Such electronics play a key role in automotive electric powertrains, to convert currents and voltages and to control the electric motor (Çaǧatay Bayindir et al 2011; Emadi et al 2008). Important production steps required for the power module are not covered

Methods

Results

Conclusion