Loss‐of‐life investigation of EV batteries used as smart energy storage for commercial building‐based solar photovoltaic systems

Abstract

This study presents a loss-of-life (LoL) analysis for electric vehicle (EV) batteries, when they are being used as smart energy storage (SES) systems in a typical solar photovoltaic (PV) system installed in building environment. EVs can be considered as ideal energy storage for solar PV system installed in commercial/office buildings. This is attributable to the fact that the idle time-period of the EVs during the daytime, and the time-period during which the solar PV requires the energy storage intersect perfectly. However, it is to be demonstrated that using EVs as SES for solar PV system in commercial/office buildings would not have a significant impact on the battery lifetime and driving range of EVs. Hence, LoL analysis is essential to get a clear picture on the expected LoL for the EV batteries when EVs are used as SES for solar PV. Furthermore, the LoL of the individual EV batteries depends on the priority criteria used for charging/discharging the EVs, namely time coordinated and power coordinated vehicle-to-grid (V2G) algorithms. Hence, a comparison of LoL for different types of EVs while using different priority criteria in both the types of V2G is presented.

Enhancing conventional battery and contact line hybrid tram system with accelerating contact lines

“…where T E is the tractive effort (N) as given in (1), η tr is the electrical-mechanical tractive power conversion efficiency, v is the velocity (m/s), t start is the time at which the tram starts moving from its current station, t stop is the time at which the tram stops at the next station, P ax is the auxiliary power, η ax is the efficiency of the auxiliary system and dt is the change in time (s). A tram manufactured by Hyundai Rotem Company was used in this study; its details as used in the simulation are given in Table 2 and in Fig.…”

“…Owing to the petroleum energy price and security concerns, growing mobility demand and its associated traffic congestion and air pollution in urban areas, efficient and clean urban transportation systems have drawn the attention of authorities, companies, and researchers [1, 2, 3]. Urban transportation is among the highest energy‐consuming industries, its associated air pollution caused by petroleum fuelled vehicles is on the rise [4, 5].…”

Evaluation of bi‐directional single‐inductor multi‐input battery system with state‐of‐charge balancing control

“…Battery systems have advanced especially in the past two decades and have been developed for a wide range of applications, such as electric vehicles, consumer electronics, and medical devices, among others [1][2][3][4][5][6][7][8]. With the increasing demand for cost and size reduction and improved performance, additional advanced functions in a smaller size are increasingly being included in advanced battery systems.…”

Agent-based modelling of electric vehicle charging for optimized charging station operation

“…where SI j is the measured solar irradiance during the j th time interval; β pv is the temperature coefficient for the module's efficiency; T amb, j is the measured ambient temperature during the j th time interval; T amb,rated is rated ambient temperature (30 o C) and P pv,peak is the maximum power generated under standard test conditions [98]. P 1 j is the power output from the solar PV system during the j th interval before considering power conversion efficiency and P j is the power output after considering power conversion efficiency.…”

“…η conv , δ PV and η MPPT represent the efficiency of the converter, de-rating factor for PV panels and efficiency of maximum power point tracking (MPPT) respectively. The value of η conv .δ PV .η MPPT is considered to be 0.85 [98]. The objective chosen for sizing the energy storage system is to avoid a condition where the solar PV generation is higher than the total EV load and the ESS capacity is not sufficient to store the surplus energy.…”