Different energy management strategies of Hybrid Energy Storage System (HESS) using batteries and supercapacitors for vehicular applications

Allegre, A. L.; Trigui, Rochdi; Bouscayrol, Alain

doi:10.1109/vppc.2010.5729110

Cited by 52 publications

(26 citation statements)

References 12 publications

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“…There are many rule‐based and optimization control studies that are used in the literatures. The general classification of control strategy is shown in Figure .…”

Section: Power Management Control Strategymentioning

confidence: 99%

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A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

Geetha

Subramani

2017

Int. J. Energy Res.

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Summary The attention on green and clean technology innovations is highly demanded of a modern era. Transportation has seen a high rate of growth in today's cities. The conventional internal combustion engine‐operated vehicle liberates gasses like carbon dioxide, carbon monoxide, nitrogen oxides, hydrocarbons, and water, which result in the increased surface temperature of the earth. One of the optimum solutions to overcome fossil fuel degrading and global warming is electric vehicle. The challenging aspect in electric vehicle is its energy storage system. Many of the researchers mainly concentrate on the field of storage device cost reduction, its age increment, and energy densities' improvement. This paper explores an overview of an electric propulsion system composed of energy storage devices, power electronic converters, and electronic control unit. The battery with high‐energy density and ultracapacitor with high‐power density combination paves a way to overcome the challenges in energy storage system. This study aims at highlighting the various hybrid energy storage system configurations such as parallel passive, active, battery–UC, and UC–battery topologies. Finally, energy management control strategies, which are categorized in global optimization, are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.

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“…There are many rule‐based and optimization control studies that are used in the literatures. The general classification of control strategy is shown in Figure .…”

Section: Power Management Control Strategymentioning

confidence: 99%

“…There are many rule-based and optimization control studies that are used in the literatures. The general classification of control strategy is shown in Figure 6 [70][71][72][73][74][75][76]. Allegra et al performed a bang-bang strategy to meet the demand by sharing energy between battery and SC.…”

Section: Power Management Control Strategymentioning

confidence: 99%

A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

Geetha

Subramani

2017

Int. J. Energy Res.

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“…Different energy management strategies have been reported in the literature for controlling power sharing in the multi-source electric vehicles [15], [36]. The basic principle of all these control strategies is that supercapacitor supports high power peaks during acceleration and braking conditions, and battery ensures the average power needed by the electric vehicle [15], [37]. Many authors have studied and classified the EMS's for hybrid embedded power supplies, grouping them into two main kinds: rule-based control and optimization-based strategies [21], [38].…”

Section: Energy Management Strategiesmentioning

confidence: 99%

“…However, the supercapacitor reaches their lower energy limitation rather than their higher limitation because in the most of driving cycles the traction power requirements is usually more important than the recovered power. For this reason, the setting of supercapacitor energy during electric vehicle operating is ensured by the recharge of supercapacitor from the battery during standstill of the electric vehicle [37]. As a result, the supercapacitor SOC at the end of the driving cycle can be the same as that in the beginning of the cycle [48], [49].…”

Section: B Energy Management Strategy Based On Frequencymentioning

confidence: 99%

Combined Optimal Sizing and Control of Li-Ion Battery/Supercapacitor Embedded Power Supply Using Hybrid Particle Swarm–Nelder–Mead Algorithm

Mesbahi

Khenfri

Rizoug

et al. 2017

IEEE Trans. Sustain. Energy

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This paper examines and optimizes parameters that affect the sizing and control of a hybrid embedded power supply composed of Li-ion batteries and supercapacitors in electric vehicle applications. High demands including power and energy density, low charge/discharge power stress on the battery (long lifetime), lightweight design and relatively modest cost at the same time cannot be provided solely by batteries or supercapacitors. For this reason, we propose the use of a Li-ion battery/supercapacitor hybrid embedded power supply for an urban electric vehicle. The sizing process of this system including the optimization of the power sharing is done thanks to a developed hybrid Particle Swarm-Nelder-Mead (PSO-NM) algorithm involving multi-objective optimization. This approach also allows us to optimize the proposed energy management strategies based on frequency rule-based control and different ways of supercapacitors energy regulation. Obtained results show that the hybrid embedded power supply with the proposed control strategies is able to offer the best performances for the chosen electric vehicle in terms of weight, initial cost and battery lifetime.Index Terms-electric vehicle, hybrid embedded power supply, Li-ion battery, supercapacitor, energy management strategy, hybrid (PSO-NM) optimization algorithm. I. NOMENCLATURE SymbolsAerodynamic drag force (N) Tire rolling resistance force (N) Gravitational resistance force by slope in the road (N) Acceleration force (N) Air density (kg/m3) S Vehicle frontal surface ( 2 ) Penetration air coefficient 0 , 1 Rolling resistance coefficients ℎ Vehicle forward speed (m/s) M Vehicle mass (Kg) Electric vehicle power (W) Final value of the energy consumption in one mission (J) _ Series battery cells in one branch _ Branches number in parallel of battery pack _ Nominal energy of battery cell ( J) _ Battery weight ratio in energy consumption ( J/Kg) W el_ B Weight of battery cell (Kg) 0 _ Internal resistance of battery cell (Ω) U _ Nominal voltage of battery cell (V) _ Maximum of traction power (W) _ Maximum of braking power (W) _ Maximum discharge power of battery cell (W) _ Maximum charge power of battery cell (W) ∂ P W cons Battery weight ratio in discharge power (W/Kg) ∂ P W rec Battery weight ratio in charge power (W/Kg) _ Series supercapacitor cells in one branch _ Branches number in parallel of supercapacitor pack ∆ Energy requirements of supercapacitor pack to ensure transient peak power (J) − Maximum of supercapacitor energy in one cycle (J) − Minimum of supercapacitor energy in one cycle (J) _ Nominal capacity of supercapacitor cell (F) DC bus voltage ( ) Supercapacitor weight ratio in discharge power (W/Kg) Supercapacitor weight ratio in charge power (W/Kg) _ Weight of supercapacitor cell (Kg) Cutoff frequency (Hz) Maximum limit of battery discharge power (W) Maximum limit of battery charge power (W) 0− Battery power before regulation of supercapacitor energy − Recharge power needed to ensure supercapacitor energy regulation − Optimal value of supercapacitor state o...

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“…For instance, a comparison of four rule-based energy management strategies is presented in [42]. On the other hand, advanced control strategies include predictive control algorithms capable of determining a power-mix in real time, based on the expected load demand and loss models for all system components [9] or aiming at the optimization of the system's performance [43].…”

Section: Control Strategymentioning

confidence: 99%

Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging

Nájera

Moreno‐Torres²,

Lafoz

et al. 2017

Energies

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This paper focuses on Hybrid Energy Storage Systems (HESS), consisting of a combination of batteries and Electric Double Layer Capacitors (EDLC), for electric urban busses. The aim of the paper is to develop a methodology to determine the hybridization percentage that allows the electric bus to work with the highest efficiency while reducing battery aging, depending on the chosen topology, control strategy, and driving cycle. Three power electronic topologies are qualitatively analyzed based on different criteria, with the topology selected as the favorite being analyzed in detail. The whole system under study is comprised of the following elements: a battery pack (LiFePO4 batteries), an EDLC pack, up to two DC-DC converters (depending on the topology), and an equivalent load, which behaves as an electric bus drive (including motion resistances and inertia). Mathematical models for the battery, EDLCs, DC-DC converter, and the vehicle itself are developed for this analysis. The methodology presented in this work, as the main scientific contribution, considers performance variation (energy efficiency and battery aging) and hybridization percentage (ratio between batteries and EDLCs, defined in terms of mass), using a power load profile based on standard driving cycles. The results state that there is a hybridization percentage that increases energy efficiency and reduces battery aging, maximizing the economic benefits of the vehicle, for every combination of topology, type of storage device, control strategy, and driving cycle.

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Different energy management strategies of Hybrid Energy Storage System (HESS) using batteries and supercapacitors for vehicular applications

Cited by 52 publications

References 12 publications

A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

Combined Optimal Sizing and Control of Li-Ion Battery/Supercapacitor Embedded Power Supply Using Hybrid Particle Swarm–Nelder–Mead Algorithm

Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging

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