Optimising business models for AI-based battery analytics platforms: a multi-sector analysis in the Swedish electric vehicle and energy storage market
This thesis investigates optimal business models for AI-based battery analytics platforms in the Swedish electric vehicle (EV) and energy storage market. The research aims to identify the most viable models for different customer segments, including EV manufacturers, fleet operators, energy storage...
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| Tipo de documento: | dissertação |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/418272 |
| Acesso em linha: | https://hdl.handle.net/2117/418272 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Electric vehicles--Batteries Vehicles elèctrics--Bateries Àrees temàtiques de la UPC::Enginyeria mecànica |
| Resumo: | This thesis investigates optimal business models for AI-based battery analytics platforms in the Swedish electric vehicle (EV) and energy storage market. The research aims to identify the most viable models for different customer segments, including EV manufacturers, fleet operators, energy storage system operators, and battery manufacturers. The study employs a mixed-methods approach, combining qualitative interviews, a quantitative survey of 40 industry professionals, and financial modeling with sensitivity analysis. A comprehensive literature review on battery degradation, AI applications, and existing business models provides the theoretical foundation. Results indicate that different customer segments have varying needs for battery analytics solutions. The subscription-based model emerges as the most promising for sectors with high adoption rates and recurring usage, projecting the highest long-term revenue growth. The pay-per-use model suits dynamic sectors like energy storage, offering flexibility with moderate revenue projections. Sensitivity analysis reveals that subscription-based and pay-per-use models are highly responsive to adoption rates and operational costs. Outcome-based and one-time license models show more stability but limited scalability, suiting conservative sectors like battery manufacturing. The study concludes that no single business model fits all segments. It recommends focusing on the subscription-based model for high-growth areas, complemented by pay- per-use options for dynamic sectors. Outcome-based models are advised for segments prioritizing measurable improvements, while one-time licenses can be used for specific short-term cash flow needs. These findings provide valuable insights for companies developing AI-based battery analytics platforms, offering guidance on tailoring business models to different market segments within the EV and energy storage ecosystem in Sweden. |
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