One of the most important environmental problems in large cities is the vehicular emission. Electric Vehicles (EVs) are a growing alternative for internal combustion engine (ICE) vehicles. Since this kind of vehicle has low autonomy yet, it is important to optimize energy consumption, for instance by planning a suitable infrastructure of battery recharge and/or battery-switch stations. This paper presents an architecture for EV simulation, important to analyze traffic flow, its dynamics and the performance when there are obstructions or intense traffic. There are several tools for traffic simulation, SUMO (Simulation of Urban MObility) is one of them. But none of the existing traffic simulators integrates models of EV that allow, for example, perform simulation studies regarding energy consumption. SUMO is a portable open source simulator with multi-modal traffic feature capabilities that permit the simulation of various types of vehicles. This work is an extension of the SUMO, two-dimensional (2D) vehicular simulation package. To allow the simulation of energy consumption of EV, two extensions were incorporated in SUMO: EV models and modeling of altitude, transforming SUMO into a three-dimensional (3D) simulator. The energy model effectiveness and correctness with 3D capabilities has been validated using two driving schedules (Urban Dynamometer Driving Schedule and Highway Fuel Economy Driving Schedule). This new tool will also support the study of better routes choice in 3D environment with EV aiming minimum energy consumption.

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

Current smart factories, and technologies such as cyber-physical systems and Internet of Things have transformed the working environments, that now incorporate digital machines with information technology. Integrated automated systems require being accurate and dependable even though operating in a non-predictable environment. The needs for testing, quality assurance, and maintenance for such types of systems are continuously increasing, and testing frameworks are often used to execute automated tests on those systems. With the objective of providing a support and enabling infrastructure for testing cyber-physical systems in closed-loop, it was designed and developed the KhronoSim testing platform. This paper presents the KhronoSim platform and architecture. It is modular, extensible and usable in multiple application domains. It features real-time control and enables the joint and flexible integration of simulation models and physical components or subsystems to build a closed-loop test environment. Furthermore, the paper presents a case study, displaying the flexibility and modularity of the KhronoSim platform and its capacity of integrating solutions from external sources, simulators, communication protocols, power sources, electrical inputs/outputs, demonstrating that it is a robust platform that is useful for testing and simulation of complex systems, yet allowing the use of physical and virtual systems alike.

This paper proposes an iterative learning approach to learn a fuzzy system composed of a sum of multiple univariate zero-order Takagi-Sugeno (T-S) fuzzy systems. The learning algorithm is based on the backfitting algorithm, and new fuzzy rules are iteratively added based on a novelty detection criterion, which gives the novelty degree of a new data by a value between zero and one, allowing an easier rule creation threshold's definition. In order to validate the performance of the proposed approach, 10 benchmark data sets are used to compare the proposed approach with two well-known state-of-the-art methods, the Extreme Learning Machine (ELM), and the Support Vector Regression (SVR), and with the GAM-ZOTS approach, which model is similar to the proposed approach. From the results, it is concluded that the proposed approach outperforms ELM, SVR and GAM-ZOTS in almost all data sets.