This paper proposes a scheme for changing the operation modes of bidirectional interleaved DC/DC converter used in battery powered electric vehicles (EVs). The proposed pattern changes the converter operation from boost to buck according to the DC-link voltage value. This strategy is suggested to save power and increase efficiency of the EV energy storage system (ESS). A buck-boost DC/DC converter is adopted to boost the DC-link voltage in driving mode, and to charge the battery in braking mode. In boost mode, PI control of DC-link voltage is implemented to maintain constant DC-link voltage in driving operation. In buck mode, PI control of battery voltage is applied to reduce ripples of battery voltage. In both modes of operation, current control schemes are applied to keep equally sharing of battery current between converter modules. To validate the performance of the proposed scheme, a simulation is performed and verified with EV propulsion system.

Ubiquity, heterogeneity and dense deployment of sensors have yielded the Internet of Things (IoT) concept, which is an integral component of various smart spaces including smart cities. Applications and services in a smart city ecosystem aim at minimizing the cost and maximizing the quality of living. Among these services, waste management is a unique service that covers both aspects. To this end, in this paper, we propose a WSN-driven system for smart waste management in urban areas. In our proposed framework, the waste bins are equipped with sensors that continuously monitor the waste level and trigger alarms that are wirelessly communicated to a cloud platform to actuate the municipal agents, i.e., waste collection trucks. We formulate an Integer Linear Programming (ILP) model to find the best set of trajectory-truck with the objectives of minimum cost or minimum delay. In order for the trajectory assistance to work in real time, we propose three heuristics, one of which is a greedy one. Through simulations, we show that the ILP formulation can provide a baseline reference to the heuristics, whereas the non-greedy heuristics can significantly outperform the greedy approach regarding cost and delay under moderate waste accumulation scenarios.

Smart grids require high reliability and sufficient bandwidth from wireless networks to support critical real-time applications and massive smart microgrid data. In general, smart microgrids need to guarantee delays at the order of a few μs for highly delay-sensitive data delivery; as well as delays within few seconds for regular data delivery. This paper presents a framework and its performance analysis for microgrid data aggregation where the microgrid is served by a wireless heterogeneous network. Using unsupervised machine learning, the framework introduces a multi-class and delay sensitivity-aware aggregation of microgrid data within the small cells of the heterogeneous network to ensure that clustering reduces the processing time for highly delay-sensitive messages. Thus, at each Transmission Time Interval (TTI), if there is queued delay-sensitive data, they are dequeued ahead of the delay-tolerant data at the scheduler. Through simulations, we show that the proposed approach successfully reduces the queuing delay by 93% for the packets of delay-sensitive (urgent) messages and the Packet Loss Rate (PLR) by 7% when compared to the benchmark where no aggregation mechanism exists prior to the small cell base stations.

For cost-effective applications, low number of current sensors are preferable to decrease the expense and size of the electric drive system of permanent magnet synchronous machine (PMSM). The renovation of the three-phase currents depends on sensing only the dc link current. However, it is not feasible to recreate the three-phase currents continuously by applying conventional space-vector pulse width modulation (SVPWM) technique depending on space vector position. Although, two space vectors are applied to construct the reference voltage vector in any sector, sometimes the application time of any of the two voltage vectors is not sufficient to determine the dc bus current. This paper proposes a usage of active zero-states SVPWM (AZSVPWM) technique to expand the implementation time of the shortest voltage vector. Several simulation results are introduced to validate the efficiency of the suggested solution.