The architecture and design of the propulsion system of electric vehicles are highly important for the reduction of energy losses. This work presents a powertrain composed of four electric motors in which each motor is connected with a different gear ratio to the differential of the rear axle. A strategy to reduce energy losses is proposed, in which two phases are applied. Phase 1 uses a divide-and-conquer approach to increase the overall output efficiency by obtaining the optimal torque distribution for the electric motors. Phase 2 applies a genetic algorithm to find the optimal value of the gear ratios, in which each individual of each generation applies Phase 1. The results show an optimized efficiency map for the output torque and speed of the powertrain. The increase in efficiency and the reduction of energy losses are validated by the use of numerical experiments in various driving cycles.

Challenge Based Learning has been proposed as a teaching-learning technique that helps students develop skills and competencies. A challenge exposes the students to the solution of problems under conditions far out of their comfort zone. A model for the deployment of this technique has been developed at Tecnologico de Monterrey. The model takes the student through a full semester project in an industrial environment. Specific course contents are taught in a modular way during the semester. The model has been labeled i Semester. This article describes some of the challenges of the implementation of the model.

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.