Evaluation of LiFePO&lt;inf&gt;4&lt;/inf&gt; batteries for Electric Vehicle applications

Anseán, David; González, Miguel Ángel Álvarez; Viera, J.C.; Álvarez, Juan C.; Blanco, C.; García, Víctor M.

doi:10.1109/smartmile.2013.6708211

Cited by 32 publications

(25 citation statements)

References 14 publications

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“…The cells have an average weight of 0.075 kg. The specific energy under nominal conditions is about 99.2 Wh·kg −1 [17]. Cell characterization and cycle aging were conducted using a multi-channel Arbin BT-2000 battery tester (College Station, TX, USA).…”

Section: Methodsmentioning

confidence: 99%

Thermal Analysis of a Fast Charging Technique for a High Power Lithium-Ion Cell

et al. 2016

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Abstract:The cell case temperature versus time profiles of a multistage fast charging technique (4C-1C-constant voltage (CV))/fast discharge (4C) in a 2.3 Ah cylindrical lithium-ion cell are analyzed using a thermal model. Heat generation is dominated by the irreversible component associated with cell overpotential, although evidence of the reversible component is also observed, associated with the heat related to entropy from the electrode reactions. The final charging stages (i.e., 1C-CV) significantly reduce heat generation and cell temperature during charge, resulting in a thermally safe charging protocol. Cell heat capacity was determined from cell-specific heats and the cell materials' thickness. The model adjustment of the experimental data during the 2 min resting period between discharge and charge allowed us to calculate both the time constant of the relaxation process and the cell thermal resistance. The obtained values of these thermal parameters used in the proposed model are almost equal to those found in the literature for the same cell model, which suggests that the proposed model is suitable for its implementation in thermal management systems.

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Section: Methodsmentioning

confidence: 99%

Thermal Analysis of a Fast Charging Technique for a High Power Lithium-Ion Cell

et al. 2016

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“…However, these curves must be determined while the battery is at equilibrium, that is, when a charge (discharge) current is not flowing [9]. In practical circumstances, ICA and DVA analysis can be carried out when a current is flowing that is not higher than 1/25 of the capacity of the battery measured in Ah (or "C25" cycle); thus direct measurements are not reliable if they are completed in less than 25 hours [10].…”

Section: State Of the Art In The State Of Health Monitoringmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

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“…Li-ion batteries with high energy density, good life cycle performance, and low self-discharge rate [4][5][6][7] are being more widely used in electric vehicles. However, the Li-ion battery charging strategy has become the bottleneck of their application, due to the slow charging speed and uncertain effects on battery life.…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

Min

Sun

et al. 2017

Energies

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Abstract:Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users' charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.

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Evaluation of LiFePO<inf>4</inf> batteries for Electric Vehicle applications

Cited by 32 publications

References 14 publications

Thermal Analysis of a Fast Charging Technique for a High Power Lithium-Ion Cell

Thermal Analysis of a Fast Charging Technique for a High Power Lithium-Ion Cell

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization

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