Control strategy to improve EV range by exploiting hybrid storage units

Abstract

In the past, the diffusion of electric vehicles (EVs) has been hindered by energy storage limits. In fact, these are the reason for the limited EV range and the consequent range anxiety of their drivers. Thanks to constant improvements in storage system technologies over the years, in terms of both energy and power density, lithium-ion batteries (LiBs) now guarantee vehicle ranges higher than 150 km for small vehicles. Another important improvement has been achieved by the hybridisation of LiBs with other storage technologies such as electric double-layer capacitors or lithium-ion capacitors. By adding an additional storage unit (ASU) to the EV battery system, the overall efficiency increases, with a consequent gain in the vehicle's expected range. In a previous paper, an optimal sizing procedure was proposed, through which it is possible to calculate the optimal ASU mass that maximises the EV range for a given vehicle, ambient conditions, and driving cycle, which was considered to be known a priori. In the present work, a real-time implementation of the control strategy on which the optimal sizing procedure was based is proposed and analysed using the results of simulation tests.

Interdisciplinary Analysis of Social Acceptance Regarding Electric Vehicles with a Focus on Charging Infrastructure and Driving Range in Germany

“…However, despite all of the facts that should dispel any concerns in this regard, the psychological phenomenon 'range anxiety' still exists [47][48][49]. This is surprising considering the driving range of modern electric vehicles and the actual mobility behavior of potential users of electric vehicles, as seen in Figure 7.…”

Optimal Energy Storage Allocation Strategy by Coordinating Electric Vehicles Participating in Auxiliary Service Market

“…In this section, LSSVM is used for typical daily load forecasting in order to achieve a situational analysis of the intra-day EV load [14]. Also, the simulation results of the model are used to study the deviation distribution of EV participation in the ancillary services market as a sample for statistical analysis of the overall deviation distribution, which is used to calculate the size of coordinated energy storage capacity under conditional risk.…”

“…Among the results in this area, literature [12] used EVs hybrid energy storage for power regulation, but did not consider the market strategy and long-term optimal allocation scheme of energy storage; literature [13] used electric energy storage configuration to achieve the effect of risky backup, but did not consider the market situation and short-term optimal scheduling method. The existing research results have not yet been discussed in the context of practical applications in terms of market response capacity and market benefits for the participation of energy storage coordinated EVs in auxiliary services [14,15,16]. For the EV-storage participation in the ancillary service market, we have to study the variation of long-term EV response capacity to plan the energy storage construction, and the randomness of short-term EV response to set the optimal bidding strategy.…”

Multi-objective optimal scheduling of reserve capacity of electric vehicles based on user wishes

“…Batteries in electric vehicles can respond quickly to signals and most sit idle for the majority of a day, which becomes a realistic condition and favorable factor for the regulation and dispatch of electric vehicles (Cheng et al, 2021). Electric vehicles have the characteristics of both adjustable load and energy storage, so they can not only draw electricity from the power system (Cheng and Yu, 2019) but also be used to implement the vehicle to grid (V2G) function (Kempton and Letendre, 1997), which can involve in the grid as energy storage to provide auxiliary reserve services (Pavić et al, 2017;Barcellona et al, 2019;Ghosh, 2020) for the grid. However, the disorderly charging and discharging of a large number of electric vehicles may increase the operating stress of the existing grid (Singh and Tiwari, 2020), lead to an increase in peak loads, losses, and voltage violations in the distribution system (Clement-Nyns et al, 2009), and even cause the power distribution system to collapse (Shafiee et al, 2013).…”