Eco-design tool to support the design of industrial electric vehicles. The case studies of an electric shuttle and an autonomous mobile robot

The benefits of process optimization brought by multiple tools that appeared in shopfloors with the fourth industrial revolution are undiscussed; however, they need electricity to run and require critical materials. Additionally, the significant impact on sustainability that early design decisions can have over the entire lifecycle is well-recognized. The literature counts several environmental analyses of electric vehicles but narrows almost uniquely on passengers' cars. Currently, the literature should i) enwiden the range of analyzed products, ii) consider all stages of the product life cycle, iii) provide tools suitable for the early stage of design, able to return consistent results handling very little data. As electrification is concerned, in the literature there are approaches intended to assess the environmental impacts or focused on the design tool. The proposed approach, further applied to develop an eco-design tool, overcomes the existing literature by providing a tool i) able to handle few data, ii) that considers all the product lifecycle phases, and iii) allows designers to assess and compare alternative scenarios. A method is proposed, and a tool derived. Two applications concern an electric shuttle and an autonomous mobile robot; with the latter the gap of assessing the environmental impact of autonomous mobile robots is also filled. The obtained results are reasonably comparable with other existing works. Results are compared to a full LCA for the frame assembly and prove that i) the tool is reliable, and it more likely overestimates the impacts; ii) the design phase is subjected to high variability, and this affects the tool results. Future works may introduce additional types of batteries, deeper focus on the manufacturing phase; machine learning techniques may support future extension of the tool and create parametric models for conceptual and early design. The proposed method and tool can be extended to the economic sphere.