As electric mobility becomes more important every day, scientific research brings us new solutions that increase performance, reduce financial and economic impacts and increase the market share of electric vehicles. Therefore, there is a necessity to compare technical and economic aspects of different technologies for each transport application. This article presents a comparison of three bus prototypes in terms of dynamic performance. The analysis is based on the collection of real data (acceleration, maximum speed and energy consumption) under different settings. Each developed prototype uses the same bus chassis but relies on different energy storage systems. Results show that the dynamic bus performance is independent on the three energy storage technologies, whereas technologies affect the management costs, charging time and available range. An extensive experimental analysis reveals that the bus equipped with a hybrid storage (lithium-ion batteries and supercapacitors) had the most favorable net present value, in comparison with storage composed of only lead–acid or lithium-ion batteries. This result is due to the greater life of lithium-ion batteries and to the capability of supercapacitors, which reduce both batteries depth of discharge and discharge rate.

Industry 4.0 has introduced a data-driven model of production and management of goods and services. This manufacturing paradigm leverages the potential of the Internet of Things (IoT), but finding the information necessary to drive manufacturing processes can be challenging. In this context, the authors propose an innovative approach based on axiomatic design to design RDF knowledge graphs from which to extract the information needed by decision makers. This approach derives from the possibility of providing RDF knowledge graphs with an equivalent matrix representation based on axiomatic design. It allows the selection of the most reliable data sources, thereby optimizing the knowledge graph construction process using matrix algebra, minimizing redundancy and improving the efficiency of query response. The goal of the presented methodology is to address the five critical aspects of Big Data (volume, velocity, variety, value, and veracity) by preordering the knowledge graph according to the information needs of business decision makers, thereby optimizing the use of the immense wealth of information made available by the Web in design.