Energy efficient navigation management for hybrid electric vehicles on highways

Abstract

Plug-in Hybrid Electric Vehicles (PHEVs) are gaining popularity due to their economical efficiency as well as their contribution to environmental preservation. PHEVs allow the driver to use exclusively electric power for 30−50 miles of driving, and switch to gasoline for longer trips. The more gasoline a vehicle uses, the higher cost is required for the trip. However, a PHEV cannot go long with its stored electricity without being recharged. Thus, it needs frequent recharging as compared to traditional engine vehicles powered by gasoline. Moreover, the battery recharging time is usually long, which leads to longer delays on a trip. Therefore, for the deployment of the PHEV technology it is necessary to provide a flexible navigation management scheme considering an efficient recharging scheduling, which allows choosing an optimal route based on the fuel-cost and time-to-destination constraints. In this paper, we show that this PHEV navigation management problem is NP-Complete and present a formal model to solve the problem using Satisfiability Modulo Theories (SMT) that provides a vehicle driver a routing plan, as well as the potential charging points that satisfy the requirements (e.g., the maximum fuel cost and the maximum waiting time). We also present a pricebased navigation control technique to achieve better load balance for the system. Our evaluations show that the formalization can be efficiently solved even with large sizes of highway topologies and large number of charging stations.

Survey on automated symbolic verification and its application for synthesising cyber‐physical systems

“…CEGIS was also employed to synthesise model predictive controllers for a wide variety of applications, e.g. heating ventilation and air conditioning systems, autonomous vehicles control, and aircraft electric power system [115].…”

MultiCalib

“…Traffic modeling and its calibration are very important for urban transportation and planning. Recently with the increasing availability of vehicle GPS data, numerous modeling and calibration techniques have been proposed for transportation services [18], e.g., inferring taxicab passenger demand [11] [12] [21], assisting regular drivers for route planning [20]; estimating traffic volumes or speeds [4]; assigning dynamic traffic to road segments [7]. To improve the performance of these models, several calibration techniques have also been proposed, e.g., jointly calibrating both demand and supply models [6]; online calibrating based on nonlinear Kalman filtering [3]; offline calibrating based on sensitivity analyses [10]; calibrating based on fuzzy Bayesian model [17].…”

“…To improve the performance of these models, several calibration techniques have also been proposed, e.g., jointly calibrating both demand and supply models [6]; online calibrating based on nonlinear Kalman filtering [3]; offline calibrating based on sensitivity analyses [10]; calibrating based on fuzzy Bayesian model [17]. These modeling and calibration techniques can be used by many applications, e.g., navigation and dispatching [18], to improve transportation efficiency.…”

“…These two kinds of models tie demand and supply together to determine temporal propagations of traffic flows. The resulting traffic conditions, such as travel times, delay, volumes, and speeds, are used for various planning and management applications, such as navigation and dispatching [18], to improve efficiency of daily transportation.…”

State-of-charge Stream Processing and Modeling for Electric Vehicle-Based Trips

“…Our research team has been collecting the SoC records having the period of 1 second via the test drive along a target road to figure out the SoC change pattern and identify critical parameters effectively affecting battery consumption. The first analysis step is to overview the technical results from the acquired SoC stream for building a SoC-integrated navigation application [7]. Battery modeling makes it possible to design a new application for EV management and control.…”