A Fuzzy Based Safe Power Management Algorithm for Energy Storage Systems in Electric Vehicles

Galdi, Vincenzo; Piccolo, Antonio; Siano, Pierluigi

doi:10.1109/vppc.2006.364267

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…Therefore, to enlarge the EV market to cold zones, the use of EDLCs with the proposed control strategy represents a good solution. In future papers, in order to better exploit the effect of the hybrid storage system, the possibility of modifying the vehicle dynamics in terms of the maximum available torque depending on the battery, SOCs, and temperatures [28] will be taken into account. In this way, the peak shaving performed by supercapacitors could be more effective at further reducing the battery stress and improving the vehicle range and battery life.…”

Section: Discussionmentioning

confidence: 99%

Control strategy to improve EV range by exploiting hybrid storage units

Barcellona

Simone

Piegari

2019

IET Electrical Systems in Transportation

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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.

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Section: Discussionmentioning

confidence: 99%

Control strategy to improve EV range by exploiting hybrid storage units

Barcellona

Simone

Piegari

2019

IET Electrical Systems in Transportation

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“…Literature 35 proposes a novel load management solution for coordinating the charging of multiple plug‐in electric vehicles (PEVs) in a smart grid system to diminish power loss in charging. In literature, 36 a fuzzy‐based safe power management system for EVs is proposed to increase the effectiveness of energy saving in terms of speed and acceleration. These research works evaluated the approaches on EV's energy saving from the consideration of energy reservation unit, charging grid, and energy management, and focused on improving EV's energy performance with the certain hardware technologies.…”

Section: Background and Motivationmentioning

confidence: 99%

A pulse‐and‐glide ‐driven adaptive cruise control system for electric vehicle

Tian

Liu

Shi

2021

Int Trans Electr Energ Syst

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Summary As the adaptive cruise control system (ACCS) on vehicles is well developed today, vehicle manufacturers have increasingly employed this technology in new‐generation intelligent vehicles. Pulse‐and‐glide (PnG) strategy is an efficacious driving strategy to diminish fuel consumption in traditional oil‐fueled vehicles. However, current studies rarely focus on the verification of the energy‐saving effect of PnG on an electric vehicle (EV) and embedding PnG in ACCS. This study proposes a pulse‐and‐glide‐driven adaptive cruise control system (PGACCS) model, which leverages the PnG strategy as a parallel function with cruise control (CC) and verifies that PnG is an efficacious energy‐saving strategy on EVs by optimizing the energy cost of the PnG operation using Intelligent Genetic Algorithm and Particle Swarm Optimization (IGPSO). This article builds up a simulation model of an EV with regenerative braking and ACCS based on which the performance of the PGACCS and regenerative braking is evaluated; the PnG energy performance is optimized, and the effect of regenerative braking on PnG energy performance is evaluated. As a result of PnG optimization, the PnG operation in the PGACCS could cut down 28.3% energy cost of the EV compared with the CC operation in the traditional ACCS, which verifies that PnG is an effective energy‐saving strategy for EVs and the PGACCS is a promising option for EV.

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“…The following section describes vehicle characteristics (case study) in conjunction with the energy storage system and the Energy Management Strategy (EMS). An energy management strategy is one that allows B and UC operating restrictions to be met in order to operate both sources safely to meet the load demand [14,17,18,20]. Energy management strategies are aimed at improving vehicle performance, taking care of battery life, and increasing autonomy.…”

Section: Battery/ultracapacitor Configuration (C-b/uc)mentioning

confidence: 99%

Performance Analysis of a Hybrid Electric Vehicle with Multiple Converter Configuration

et al. 2020

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The use of electric vehicles and their various configurations is seen as a major alternative in efforts towards reducing pollutant emissions from motor vehicles that continue to use fossil fuels. Electric transport technology presents more efficient means of energy conversion in vehicles: electric (EV), hybrid (VH), and hybrid electric (HEV) vehicles. For example, the energy storage system in the latter can be made up of ultracapacitors (UCs), batteries (Bs), and fuel cells. This work focuses on HEVs powered by batteries and ultracapacitors. In particular, the multiple converter configuration (C-CM) for the HEV powertrain system is analyzed using electric models of the vehicle powertrain components. To analyze the multiple converter configuration, parameters of a vehicle taken from the literature and the electrical model of the configuration were developed. With the above, the proposed configuration was evaluated before driving cycles (CITY II and ECE) and the configuration performance was compared with respect to other configurations. In the C-CM model, limitations in the choice of the number of Bs and UCs were observed in the powertrain depending on the maximum power of both energy sources and vehicle load demand. The results show that more energy is extracted from the batteries in the ECE cycle than in the CITY taking into account that the batteries are used as the main power source. C-CM results compared to other configurations show that energy extracted from batteries in the CITY is the same across all configurations. While energy consumption is lower in the ECE, C-CM results were not very significant compared to other configurations. However, the C-MC has the advantage of having better power flow control due to having two converters, thus improving HEV safety.

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A Fuzzy Based Safe Power Management Algorithm for Energy Storage Systems in Electric Vehicles

Cited by 15 publications

References 13 publications

Control strategy to improve EV range by exploiting hybrid storage units

Control strategy to improve EV range by exploiting hybrid storage units

A pulse‐and‐glide ‐driven adaptive cruise control system for electric vehicle

Performance Analysis of a Hybrid Electric Vehicle with Multiple Converter Configuration

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