Abstract

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V. "p"open acce

Abstract

This paper presents a reference architecture for interoperability testing of electric vehicles as well as their support equipment with the smart grid and the e-Mobility environment. Pan-European Electric Vehicle (EV)-charging is currently problematic as there are compliance and interoperability issues on different communication levels and among the different domains comprising the eMobility system. The discussed reference architecture is composed out of three layers that enable addressing a direct mapping of interfaces, functions and services, as well as real world actors and/or laboratory equipment. Utilizing this architecture enables cross-domain co-simulation for interoperability within the electric mobility and the smart grid environment. Given the future challenges that rise to interoperability testing and the fact that certain aspects are yet unknown or rarely defined, the discussed architecture presents a very modular and extendable approach. © 2015 IEEE.

Abstract

Battery thermal management is essential to efficienct and safe operation of Li-ion batteries, particularly in challenging applications such as fast charging. This work proposes a thermal management control framework based on MPC. The novelty of this approach resides in the focus on incorporating disturbance prediction into the control decision. Simulations show that this methodology allows for (1) lower peak and mean battery temperatures during fast charging for a similarly sized battery thermal management system; or (2) battery thermal management system downsizing for similar thermal performance.

Abstract

Model based development methodologies have strong potential in reducing development effort of complex automotive systems. This paper presents a model based development methodology used to develop battery state estimation algorithms, and steps through all the steps required for this. The benefits of the mixed simulation and testing methodology are outlined using a State of Charge estimation algorithm development example. Overall, this development methodology leads to safer, more efficient and more affordable battery packs, enabling faster market update of electric vehicles.

Abstract

The strong interaction between electric vehicles and the charging infrastructure presents new challenges towards system-level optimization. Moreover choices between the power level, duration, and frequency of charging events can have direct impact both on energy efficiency, as well as the overall lifetime of vehicles. This paper presents a preliminary study into the optimization scheme for such systems, with the focus on an electric bus application. The results illustrate an energy saving when comparing the EMS and baseline strategy of ~ 1.6%improvement measured in kWh/km, alongside a potential cycle life prolongation factor improvement of the EMS compared to the baseline is 1.5 in average.

Abstract

WOS: 000513381900001 In this study, a two-stage battery charger architecture with high-efficiency, multi-input, and output half-bridge LLC (HBLLC) resonance converter that performs a wide load range is proposed. The first input of the HBLLC is provided by the photovoltaic (PV) panel assembly on the vehicle. A high efficiency and fast maximum power point tracking (MPPT) algorithm has been developed for the PV panel to operate at the maximum power point. The other input is supplied by a grid-connected AC-DC bridgeless power factor correction (PFC) converter, which is controlled with the average current mode (ACM) control method. The most important feature that distinguishes the designed topology from previous studies is that it charges the low-voltage battery through the PV panel. In previous studies, the low-voltage battery was being charged via the high-voltage battery. This allowed the high-voltage battery to transfer power to the low-voltage battery even when it was not charged. However, in the proposed architecture, the low-voltage battery is fed by a PV panel. This condition allows the electric vehicle to take more miles with a single charge process. Furthermore, the proposed architecture reduces energy costs in the long term by providing some of the energy demanded from the grid. In addition, the proposed integrated battery charging circuit is intended to reduce the cost of additional cables. The system is designed as 3.1 kW power and operated under no load to full load. As for the performance of the proposed architecture, the peak efficiency of the LLC resonant converter is 95.3%. In addition, peak efficiency of the AC-DC bridgeless PFC converter is 97.3%, while the power factor is higher than 0.99, input current total harmonic distortion (THD) is less than 5%, MPPT method accuracy is higher than 99%, and output voltage ripples (Delta V) is less than 1 V.

Abstract

Turksoy, Omer/0000-0003-4620-4503 WOS: 000492797300026 The energy efficiency of the battery charging system directly affects the distance which electric vehicles can get with per charge process. In addition, reducing current harmonics distortion (THD) increases the electrical quality and power conversion performance. This paper proposes intelligent control of high-efficiency two-stage battery charger topology for electric vehicles (EVs). In the first stage, for low harmonics of input current, high power factor and high efficiency, the average current mode controlled ac/dc boost power factor (pf) correction method constructed and analyzed with 98% efficiency and the power factor is achieved higher than 0.99. Additionally, in the second stage, for high energy efficiency, higher battery lifespan and allowing EVs to get more miles with per charge, a half-bridge LLC resonant converter controlled with a designed artificial neural network (ANN). Moreover, the LLC resonant converter controlled under dynamic load to analysis system from no-load to full-load. A 3.1 kW powered system has been designed for this study, the efficiency of the system has been calculated based on European efficiency standards and the peak efficiency of the whole system is 96.2%, the load voltage ripple is less than 0.5 V and also total harmonic distortion of the source current is less than 5%. (C) 2019 Elsevier Ltd. All rights reserved.