Battery lifetime of electric vehicles by novel rainflow-counting algorithm with temperature and C-rate dynamics: Effects of fast charging, user habits, vehicle-to-grid and climate zones

Fioriti, Davide; Scarpelli, Claudio; Pellegrino, L.; Lutzemberger, Giovanni; Micolano, Enrica; Salamone, Sara

doi:10.1016/j.est.2022.106458

Cited by 15 publications

(5 citation statements)

References 42 publications

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“…Similarly, the battery lifetime was extended compared to the second scenario by 94.274%, from 4.087 to 7.940 years in winter. Our results are aligned with Davide Fioriti et al (2023) and Lehtola et al (2019) in that the battery lifetime is influenced by the usage patterns and DOD, and that the fewer the daily battery cycles, the higher the battery lifetime, which reach more than 20 years [62,63]. The utilization pattern of the battery differs from winter to summer.…”

Section: The Ess Lifetime and Energy Lossessupporting

confidence: 91%

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Al Muala,

Bany Issa,

Bello Bugallo

2024

Batteries

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Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.

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Section: The Ess Lifetime and Energy Lossessupporting

confidence: 91%

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Al Muala,

Bany Issa,

Bello Bugallo

2024

Batteries

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“…The factors affecting the electric power demand of EVs mainly include three aspects: power battery, charging facilities, and user behavior [16]. The real-time road traffic in the region is obtained through the wireless mobile communication system equipped with EVs.…”

Section: Model Constructionmentioning

confidence: 99%

EV Charging Path Distribution Solution Based on Intelligent Network Connection

Wang

Shen

2023

Mathematics

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The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.

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“…Other authors analyzed the impact of smart charging stations on the distribution sector [11], low-voltage grids [12], and advanced charging behavior archetypes different from current "liquid fuel" mental models [13]. Another important subject of research is the vehicle-to-grid (V2G) concept, considering BEVs connected to the grid as virtual power plants [14], even though this bidirectional use of BEVs has to be carefully evaluated in terms of battery degradation [15]. This paper will propose only a power balance, based on the Italian production/load profile, and not include possible difficulties arising on the transmission and distribution grid, as well as system security, which will be addressed in future research.…”

Section: Introductionmentioning

confidence: 99%

Managing BEV Charge to Obtain a Positive Impact on a National Power System

Barsali,

Ceraolo,

Pasini

et al. 2024

Energies

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This paper’s research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car’s charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time.

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Battery lifetime of electric vehicles by novel rainflow-counting algorithm with temperature and C-rate dynamics: Effects of fast charging, user habits, vehicle-to-grid and climate zones

Cited by 15 publications

References 42 publications

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

EV Charging Path Distribution Solution Based on Intelligent Network Connection

Managing BEV Charge to Obtain a Positive Impact on a National Power System

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