Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles

Zhu, Tao; Wills, Richard; Lot, Roberto; Kong, Xiaodan; Yan, Xingda

doi:10.1016/j.energy.2021.119851

Cited by 56 publications

(24 citation statements)

References 43 publications

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“…All of them represent different methods of solar energy storage. The latest literature shows different ways of battery storage, both utilityscale [47] or small scale as home batteries [48] or mobile batteries used in electric vehicles [49]. Naturally, these storage methods may refer to the energy storage from both solar and wind power plants.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Nature-Inspired Optimization on Integrative Capacity of Renewable Energy in the Western Balkan

Stevović¹,

Kirin²,

Bozic³

2021

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Two artificial intelligence models for the integration of renewable energy sources are developed within this research to contribute to the European Green Plan realization. The review of renewable energy natural potential, on one hand, and installed capacity, on the other hand, in the Western Balkans and twenty-eight European countries is done within this research, as well as emissions. The analyses show that the European countries, sometimes even with lower natural potential in renewables, have installed much more renewable capacities than the Balkans countries with much higher natural potential. According to this, the first artificial intelligence model is developed based on multi-criteria linear regression analysis. This model relies on the correlation between the relevant regressors, i.e. relevant input variables for twenty-eight European countries and the same regressors for a particular Balkans country. Its goal is to find the maximum possible integrative renewable capacity in a Balkans’s country within the real socio-economic environment. The second artificial intelligence model is developed based on multi-criteria evolution genetic algorithms. Its goal is to find the maximum possible integrative renewable capacity within a real electric power system. Nature-inspired optimization is applied. From the framework of a given large number of generations, technical combinations of the degree of renewable energy sources integration, the best populations, i.e. combinations are selected. As nature selects from many generations and allows the best to survive and punishes the „weakˮ, in our case, „weak” combinations are those failing to meet the given conditions and limitations of the real electric power system. A new methodology is offered. Theoretical general formulas are given for both models. Developed models are tested on a numerical experiment of solar energy integration in the Serbia case study. Analyses of sensitivity prove that both models are applicable for all renewable energy sources and countries or regions.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Nature-Inspired Optimization on Integrative Capacity of Renewable Energy in the Western Balkan

Stevović¹,

Kirin²,

Bozic³

2021

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“…The three weighting parameters Q 2,∆D , Q 2,SOA , and Q 2,loss are set according to industrial average prices for lithium-ion batteries, ultracapacitors, and energy from the electrical grid [46,47], such that the battery aging, ultracapacitor aging, and energy loss terms are all equally weighted based on their real-world values. Then, the remaining term Q 2,SOC is used to tune the strategy.…”

Section: Dynamic Programmingmentioning

confidence: 99%

“…Alternatively, we can consider that battery and UC components use the pricing of reference [50] rather than [46] while maintaining V nrg = $0.1067/kWh; the increased battery and ultracapacitor prices from [50] result in a payback time of 6.9 years for the N pc = 10 UC and 13.7 years for the N pc = 100 UC. On the other hand, the component prices of [51] would indicate that the HESS is not beneficial under any circumstance.…”

Section: Cost-benefit Analysismentioning

confidence: 99%

“…Finally, the cost-benefit of the HESS is determined. First, the value of the HESS is determined based on the industrial average price per kWh of $300/kWH for lithium ion batteries and $15, 000/kWh ultracapacitors from [46] and on the battery and ultracapacitor size given in Section 2.1. This gives a battery value of V batt = $38,760 and an ultracapacitor value of V uc = $13,021.…”

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confidence: 99%

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A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

Mallon

Assadian

2022

Energies

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Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy.

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“…In [8], HESSs of battery-SC were used with four different semi-active topologies, while SC size was optimized by dynamic programming (DP) approach. HESS sizing optimization and complete analysis for battery-SC HESS using DP approach to minimize vehicle lifetime costs was also presented in [9]. It refers to the main factor affects the EV cost is battery degradation whose impact index reaches to 89% of HESS costs among eight sensitive factors: driving cycle, driving range, HESS topology, bus voltage, DC/DC conversion efficiency, battery price, SC price and DC/DC converter price.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Thermal Performance and Lifetime Cycles of Li-ion Battery in Electric Vehicles

Fayez

Shafei

Ibrahim

2021

IJRER

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Hybrid energy storage system has essential priority in Electric Vehicle applications. Therefore, the design of an appropriate power sharing algorithm among energy storage components is necessary to improve battery thermal performance and provide extra extension of battery lifetime cycles. This paper presents an analytical study on the effect of using wavelet decomposition-based power sharing algorithm to force the high frequency component to be fed by the supercapacitor and accordingly reduces the thermal stress on the battery. The proposed approach was investigated by applying it on electric vehicle model in ADVISOR Tool/MATLAB using different driving profiles such as Urban Dynamometer Driving Schedule profile, Highway Fuel Economy Test, New York City Cycle, Los Angeles 1992 and new European driving cycle. The results declare that by using proposed power sharing algorithm, the working temperature of lithium battery decreases significantly while battery lifetime cycles increase, apparently. For urban dynamometer driving schedule, the operating temperature of lithium battery is improved much at maximum decomposition levels reaching to only 25.6 °C compared to 35 °C. In addition, the battery lifetime cycles increased from 2213 to 2585 cycles. Neural Networks pattern recognition tool is also applied to classify the driving cycle to the nearest reference cycles chosen to represent the different driving conditions which help to detect the appropriate wavelet decomposition level, achieving better battery thermal performance and battery lifetime cycles.

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Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles

Cited by 56 publications

References 43 publications

Artificial Intelligence and Nature-Inspired Optimization on Integrative Capacity of Renewable Energy in the Western Balkan

Artificial Intelligence and Nature-Inspired Optimization on Integrative Capacity of Renewable Energy in the Western Balkan

A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

Enhancing Thermal Performance and Lifetime Cycles of Li-ion Battery in Electric Vehicles

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