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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

n-ev-retrofits

Intelligent Multi-Vehicle Planning

by with No Comment Automation

Optimizing Energy and Battery Health Using Route, Allocation and Velocity Planning for a Multi Electric Vehicle System

The rising need for green transport drives the imperative to refine electric vehicle (EV) operations, prioritizing both single charge range and battery lifespan. A comprehensive approach is presented to enhance the range of EVs and battery life by optimizing energy consumption and charging/discharging behavior. The problem focuses on the selection of energy-efficient routes, customer allocation, and driving speed for a multi-EV system. Constraints include: depth and rate of charging/discharging, EVs’ capacity and quantity, customers’ availability and demand, speed and acceleration limits, bounds on input torque and its rate of change, and traffic signals, while uncertainties cover slippery road surface and grade. Dijkstra’s algorithm, mixed integer linear programming (MILP) algorithm, and model predictive control (MPC) algorithm are used to perform the task. Energy consumption improved by 6.6%, when compared with integrated routing and driving technique using Clarke and Wright (C&W) and Pontryagin’s minimum principle (PMP), respectively. Battery effective capacity and lifespan increase from 27 to 31.2 kWh and 7.12 to 8.85 years, respectively. The improvement in range per charge and over lifespan compared with commercially available data of Dacia Spring Electric 45 and Leapmotor T03 further strengthens the claim. Almost the same execution time is obtained for the proposed integrated strategy when compared with the standalone routing and driving techniques.

https://ieeexplore.ieee.org/abstract/document/11096722