The energy transition in the transportation sector has led to the growing adoption of hybrid and electric vehicles, which in turn has created a major challenge in managing end-of-life lithium-ion batteries. Recycling this component is an essential response to this challenge, but its economic viability remains fragile, particularly due to the high costs and risks associated with the preliminary disassembly stage. At the same time, this disassembly, which is performed mostly manually, exposes workers to significant ergonomic stresses that remain poorly documented in the scientific literature.
This thesis makes two main contributions. The first is the development of a probabilistic model for estimating manual disassembly time, applicable as early as the product design phase, which incorporates disassembly difficulties and the variable condition of end-of-life components. This model is based on a probabilistic correction factor generated by Monte Carlo simulation using a distribution parameterized from empirical assembly tables (Boothroyd, Dewhurst, & Knight, 1994). This method produces a time interval rather than a single deterministic value. The second contribution lies in the original integration of this time interval with three postural assessment methods. The RULA, KIM-MHO, and OCRA tools enable the quantification of ergonomic risks associated with battery pack removal tasks, considering the variability of operational conditions.
The methodology employs a multiple-case study involving five hybrid and electric vehicles (Tesla Model 3, Nissan Leaf, Ford Escape, Mitsubishi Outlander, and Mitsubishi Outlander “fast charge” version) for battery pack removal tasks. A reference disassembly time is calculated from assembly tables or predetermined times, then adjusted by a probabilistic factor. The model is validated through a timed video analysis. The ergonomic analysis is conducted using a digital human model in CATIA V5 for two anthropometric profiles of the Canadian population (5th percentile female and 95th percentile male).
The simulation results reveal a factor of approximately four between the median disassembly times of the fastest and slowest vehicles in the sample, highlighting the decisive influence of design choices on disassembly productivity. Regarding ergonomics, the three methods converge on a diagnosis of high risk of musculoskeletal disorders for all vehicles studied that require a working posture above the heart. These results confirm that worker productivity and health are two inseparable dimensions of sustainable disassembly and support the integration of disassembly and ergonomic criteria as early as the design phase of electric vehicle batteries.
Alix, E. (Author),
Tahan (Supervisor),
Nadeau (Co-supervisor) &
Kenné (Co-supervisor),
30 Jun 2026Student thesis: Master's thesis › Master in Engineering: Mechanical Engineering