Dynamic Molecular Switches Drive Negative Memristance Mimicking Synaptic Behavior

To realize molecular scale electrical operations beyond the von Neumann bottleneck, new types of multi-functional switches are needed that mimic self-learning or neuromorphic computing by dynamically toggling between multiple operations that depend on their past. Here we report a molecule that switches from high to low conductance states with massive negative memristive behavior that depends on the drive speed and the number of past switching events. This dynamic molecular switch emulates synaptic behavior and Pavlovian learning and can provide all of the fundamental logic gates because of its time-domain and voltage-dependent plasticity. This multi-functional switch represents molecular scale hardware operable in solid-state devices opening a pathway to dynamic complex electrical operations encoded within a single ultra-compact component.

Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms

Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.

Control of Three Phase BLDC Motor for Electric Vehicles by using Four Quadrant Operation

In this paper the four quadrant operation implemented for the recovery of electric vehicles. BLDC motor is used in EV, and bi-directional DC-DC converter is connected to the VSI (voltage source inverter). Bi-directional DC-DC converter performed to modes that is, buck mode and boost mode, energy is recovered through this mode. In buck mode utilized the energy for drive the motor and in the boost mode regeneration of energy and charged the battery. This proposal operated in MATLAB\Simulink software. By using this method we can improve the energy management of electric vehicles when vehicles in motoring mode bi-directional converter did buck operation and utilized energy for driving vehicles. During electric vehicles often start and stop, this operation proposes recovery of kinetic energy of motor and stored it in battery through the regenerative braking. Through electric vehicles going on downhill, drive speed develops more than reference speed, controlled speed offer energy and this energy return to battery.

Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive

An inherent torque ripple characterizes switched reluctance technology from conventional technology. The ultimate aim of this paper is to reduce the torque ripple of the switched reluctance motor drive using genetic neural network controller based direct torque scheme. In the proposed controller network appropriate bits of data are chosen for training and testing. The proper selection of the learning rate and momentum will help in weight adjustment. Here the error is reduced which proves that the selection of voltage vectors from the vector table is precise and its results in better torque response over a wide range of speed. The simulation results reveal that the torque ripples vary between 3.25% to 1.7% for the variation in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.7% to 2.1%. Both the simulation and hardware results illustrate the efficiency of the controller.

Torque Ripple Minimization in Switched Reluctance Motor using ANFIS Controller

An inborn torque swell portrays changed hesitance innovation from traditional innovation. A definitive target of this paper is to minimize the torque wave of the exchanged hesitance engine drive utilizing Artificial Network Fuzzy Inference System based direct torque conspire. In the proposed controller arrange proper bits of information are picked for preparing and testing. The best possible choice of the learning rate and energy will help in weight change. The Intelligent controller gives high power over motor torque and speed, lessens rise time just as overshoot. Here the blunder is decreased which demonstrates that the determination of voltage vectors from the vector table is exact and its outcomes in better torque reaction over a wide scope of speed. The reenactment results uncover that the torque swells fluctuate between 3.75% to 2.25% for the variety in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.9% to 2.4% for the variation in speed. Both the recreation and equipment results delineate the effectiveness of the controller.

Speed Acquisition

Speed is a salient feature of modern financial markets. This paper studies investors’ speed acquisition together with their information acquisition. Speed heterogeneity arises in equilibrium, fragmenting the information aggregation process with a nonmonotone impact on price informativeness. Various competition effects drive speed and information to be either substitutes or complements. The model cautions the possible dysfunction of price discovery: An improving information technology might complement speed acquisition, which shifts the concentration of price discovery over time, possibly hurting price informativeness. Novel predictions are discussed regarding investor composition and their investment performance. This paper was accepted by Gustavo Manso, finance.