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Connectivity-Driven Energy Optimization

by with No Comment Automation

Energy economization using connectivity-based eco-routing and driving for fleet of battery electric vehicles

Energy economizing using Connectivity-based eco-Routing and Driving (CeRD) is a key to conserve the resources of a fleet. This work takes maximum advantage by advising optimal energy-consumption-based route and velocity. Product delivery to several universities by a group of battery electric vehicles (BEVs) is considered. Problem contains certain constraints, i.e., BEV load capacity, time window, resources restriction, speed bounds, battery charging/discharging limits, and traffic. Simulation of urban mobility (SUMO) is used for the estimation of traffic and routes. Selection of route is based on energy consumption of traffic’s velocity. Dijkstra’s algorithm is applied to select the route with least energy consumption, and Clarke and Wright (C&W) algorithm is used to find the optimal sequence of visiting universities. Pontryagin’s minimum principle (PMP) is then applied to find the optimal velocity profiles for BEVs on the optimized routes. Results have shown 60%–70% reduction in energy consumption along with improvement in charging rates when compared with the vehicles without CeRD assistance in different scenarios. Furthermore, the results are validated for distance and time using online route planner, for energy efficiency using electric vehicles’ database, and for energy consumptions using a driver model. CeRD has also improved 30% route distance and 40% trip time.

https://ieeexplore.ieee.org/abstract/document/10143086

Torq

Optimized Genetic Powertrain Controller

by with No Comment AutomobileElectric Vehicles

Genetic algorithms optimized multi-objective controller for an induction machine based electrified powertrain

In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.

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EV

Guidance of air vehicles

by with No Comment AutomobileElectric Vehicles

A sliding mode approach

This paper presents a novel nonlinear guidance scheme for ground track control of aerial vehicles. The proposed guidance logic is derived using the sliding mode control technique, and is particularly suited for unmanned aerial vehicle (UAV) applications. The main objective of the guidance algorithm is to control the lateral track error of the vehicle during flight, and to keep it as small as possible. This is achieved by banking the vehicle, that is, by executing roll maneuvers. The guidance scheme must perform well both for small and large lateral track errors, without saturating the roll angle of the vehicle, which serves as the control input for the guidance algorithm. The limitations of a linear sliding surface for lateral guidance are indicated; a nonlinear sliding surface is thereafter proposed which overcomes these limitations, and also meets the criterion of a good helmsman. Stability of the nonlinear surface is proved using Lyapunov theory; control boundedness is also proved to ensure that the controls are not saturated even for large track errors. The proposed guidance law is implemented on the flight control computer of a scaled YAK-54 UAV and flight results for different scenarios (consisting of both small and large errors) are presented and discussed. The flight test results confirm the effectiveness and robustness of the proposed guidance scheme.

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