Powering up the workforce

Abstract

The growing battery industry faces an urgent need for skilled operators, driven by rapid technological development, industrial transformation, and workforce short ages. To support recruitment, training, and upskilling, reliable methods for assessing practical skills are essential - especially as future workers often come from diverse backgrounds. This thesis investigates how operator skills relevant to battery production can be effectively assessed.

A literature review identified seven categories of skill assessment methods: self assessment, test-based, human observation, performance-based, computer-aided, AI-supported, and background-based assessments. An experimental study was conducted at Battery Center Gothenburg (BCG), using self-assessments, human expert observations, and performance-based assessments. The results show that each method captures different aspects of skill: self-assessments are scalable and promote reflection but are prone to bias; human expert observations provide contextual insight but require consistency and resources; and performance-based assessments offer objective measures of real task execution, though they are time- and resourceintensive.

The findings clarify how different assessment methods can support various stages of training and evaluation in the battery industry. By understanding their respective advantages and limitations, educators and companies can implement more targeted and effective strategies to assess and develop the skills needed to meet the sector’s growing workforce demands.