AI-Based Control of Storage Capacity in High-Power-Density Energy Storage Systems, Used in Electric Vehicles

Mehraban, Abbas; Ghanbari, Teymoor; Farjah, Ebrahim

doi:10.1109/tte.2023.3287357

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…On the user and microgrid side, the main consideration is to improve power quality while making profits for users, which is to mainly control power fluctuation of the link line, reduce electricity cost, improve power supply reliability and participate in the demand-side response [90]. In addition, in order to reduce the number of traditional energy storage configurations and give play to the energy storage characteristics of demand-side resources, some scholars have built a generalized energy storage capacity configuration model based on electric vehicles and demand-side flexible loads that can be reduced or transferred [91]. On this basis, joint planning of distributed power supply and generalized energy storage can be carried out.…”

Section: Optimization Objectivementioning

confidence: 99%

Review on the Optimal Configuration of Distributed Energy Storage

Liu,

Su,

Quan

et al. 2023

Energies

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With the large-scale access of renewable energy, the randomness, fluctuation and intermittency of renewable energy have great influence on the stable operation of a power system. Energy storage is considered to be an important flexible resource to enhance the flexibility of the power grid, absorb a high proportion of new energy and satisfy the dynamic balance between the supply and demand of a system. At present, the cost of energy storage is still high, and how to achieve the optimal energy storage configuration is the primary problem to be solved. Therefore, the current research progress in energy storage application scenarios, modeling method and optimal configuration strategies on the power generation side, grid side and user side are summarized in this paper. On this basis, the shortcomings that still exist of energy storage configuration research are summarized, and the future research direction for energy storage configuration is prospected. This review can provide reference for the latest development and future research and innovation direction for energy storage configuration.

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Section: Optimization Objectivementioning

confidence: 99%

Review on the Optimal Configuration of Distributed Energy Storage

Liu,

Su,

Quan

et al. 2023

Energies

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“…EVs need high energy during steady-state driving and brief high peak power during acceleration and braking. In particular, subjecting LIBs to frequent power surges and charging cycles degrades their lifespan and overall health [4]. Standard single-source LIBs in EVs cannot simultaneously meet high power and energy demands without compromising on performance, range, or lifespan, making it challenging for them to rival traditional combustion engine vehicles (ICEVs).…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

Omakor,

Alzayed,

Chaoui

2024

Energies

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A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5 ∘C, which is the highest operating temperature attained under the proposed strategy.

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