Development of a decentralized smart charge controller for electric vehicles Tianxiang Jiang a , Ghanim Putrus b,⇑ , Zhiwei Gao b , Matteo Conti c , Steve McDonald a , Gillian Lacey b a School of Electrical and Electronic Engineering, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK b Faculty of Engineering and Environment, Northumbria University, Newcastle Upon Tyne NE1 8ST, UK c School of Design, Royal College of Art, London SW7 2EU, UK article info Article history: Received 15 April 2013 Received in revised form 19 February 2014 Accepted 20 March 2014 Keywords: Electric vehicle Battery modeling Battery cycle life Power networks Smart charging Fuzzy logic abstract Existing commercial battery charging posts for electric vehicles (EV) offer limited controllability and flex- ibility. These chargers are not designed to allow users to specify important criteria such as desired energy for next trip and waiting time whilst charging. In addition, the charging regime is not set to take into con- sideration the impact of charging (e.g. rate of charge) on the battery cycle life and the grid supply. With increased penetration of EVs and distributed generators (DG), complying with grid regulations will become more challenging, e.g. network voltage levels may deviate from the statutory limits. More- over, as the battery is the most expensive part of an EV, consideration should be given to extending bat- tery life and reduce the effective EV cost. Therefore, there is a need to develop a smart EV charge controller that can meet users’ requirements, extend battery cycle life and have minimum impact on the grid supply. In this paper, a smart controller is proposed which determines the optimal charging current based on grid voltage, battery state of health and user’s trip requirements. Models of a typical UK power distribu- tion network and an EV battery (that allows simulation of battery aging process) are developed to inves- tigate the performance of the ‘‘smart’’ charging system. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed controller. Ó 2014 Elsevier Ltd. All rights reserved. Introduction Renewable energy and electric vehicles (EV) are intended to replace conventional electricity generation and transport systems, which is expected to result in a significant reduction in greenhouse gas emissions [1,2]. However, the intermittent nature of renew- ables combined with uncontrolled EV charging can have significant adverse impacts on power networks, e.g. overloading of transform- ers and voltages exceeding the statutory limits [3–5]. Deilami and Masoum [6,7] suggested a centralized EV charge aggregator that employs ‘‘objective functions’’ to solve voltage sag problems. The aggregator collects information from every charging point (such as EV arrival, departure, charging priority and charging time) and runs the network load flow every 5 min, to generate effective commands to the chargers, in order to avoid exceeding the voltage statutory limits. Practically, this centralized control method has difficulties in responding to frequent changes in grid voltages, especially with high penetration levels of embed- ded intermittent renewable generation. In addition, it is difficult to identify an individual EV’s charging status and its user’s needs. As a result, there is a need for a decentralized control strategy to meet the user’s requirements without compromising the grid quality of supply. Singh et al. [8] developed a decentralized controller based on fuzzy systems to realise a real-time EV charging/discharging (V2G) control, where 50% of the EV battery pack energy was reserved for EV use and the rest was used to support ancillary ser- vices for the grid (e.g. voltage control). The suggested control strat- egy aims to support the grid, but does not consider the user’s requirements or the battery state of health (SOH). Battery capacity degradation affects the overall EV cost and range. Therefore, it is important to consider this aspect during charging. Battery capacity loss includes cycle loss and calendar loss [9]. Spotnitz [10] indicated that cycling causes capacity loss at a greater rate than from calendar losses alone. Marra et al. [11] and Lunz et al. [12] concluded that the four main factors that affect the battery cycle life are temperature, state of charge (SOC), charging current (normally presented as ‘‘C-rate’’) and depth of http://dx.doi.org/10.1016/j.ijepes.2014.03.023 0142-0615/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +44 (0)191 227 3107. E-mail addresses: Tianxiang.Jiang@newcastle.ac.uk (T. Jiang), ghanim.putrus@ northumbria.ac.uk (G. Putrus). Electrical Power and Energy Systems 61 (2014) 355–370 Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes