Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications

Eddahech, Akram; Briat, Olivier; Woirgard, Eric; Vinassa, Jean-Michel

doi:10.1016/j.microrel.2012.06.085

Cited by 139 publications

(65 citation statements)

References 14 publications

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“…Battery pack cells imbalance is a vital issue in the battery system life. The BMS performs several tasks such as measuring the system VIT, the cells' SoC, SoH, and RUL estimation as [50][51][52][53][54], protecting the cells, thermal management, controlling the charge/discharge procedure, data acquisition, communication with on-board/off-board modules, monitoring, storing historical data and most important task is the cell balancing [1].…”

Section: Bmss Architecture and Tasksmentioning

confidence: 99%

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

et al. 2014

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Lithium-based batteries are considered as the most advanced batteries technology, which can be designed for high energy or high power storage systems. However, the battery cells are never fully identical due to the fabrication process, surrounding environment factors and differences between the cells tend to grow if no measures are taken. In order to have a high performance battery system, the battery cells should be continuously balanced for maintain the variation between the cells as small as possible. Without an appropriate balancing system, the individual cell voltages will differ over time and battery system capacity will decrease quickly. These issues will limit the electric range of the electric vehicle (EV) and some cells will undergo higher stress, whereby the cycle life of these cells will be shorter. Quite a lot of cell balancing/equalization topologies have been previously proposed. These balancing topologies can be categorized into passive and active balancing. Active topologies are categorized according to the active element used for storing the energy such as capacitor and/or inductive component as well as controlling switches or converters. This paper proposes an intelligent battery management system (BMS) including a battery pack charging and discharging control with a battery pack thermal management system. The BMS user input/output interfacing. The battery balancing system is based on battery pack modularization architecture. The proposed modularized balancing system has different equalization systems that operate inside and outside the modules. Innovative single switched capacitor (SSC) control strategy is proposed to balance between OPEN ACCESSEnergies 2014, 7 2898 the battery cells in the module (inside module balancing, IMB). Novel utilization of isolated bidirectional DC/DC converter (IBC) is proposed to balance between the modules with the aid of the EV auxiliary battery (AB). Finally an experimental step-up has been implemented for the validation of the proposed balancing system.

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Section: Bmss Architecture and Tasksmentioning

confidence: 99%

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

et al. 2014

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“…Some common in-situ diagnosis methods include: SRCV [13][14][15][16], ICM [17,18], incremental voltage method (IVM) [19,20], EIS [13,14,21], nuclear magnetic resonance (NMR) spectroscopy [22] and x-ray computational tomography (XCT) [23][24][25]. Examples of ex-situ analysis include: x-ray diffraction (XRD) [14,16,26,27], X-ray photoelectron spectroscopy (XPS) [28,29], and scanning electron microscopy (SEM) [13,[30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries

Merla

Yufit

et al. 2016

Journal of Power Sources

130

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 Accelerated ageing of lithium-ion cells under various storage/loading conditions.  Degradation analysis was performed using various in-situ diagnosis methods.  Differential thermal voltammetry (DTV) suitable for electric vehicle application.  State-of-health estimation through quantitative analyses of DTV peak evolution. AUTHOR E-MAIL ADDRESSYu Merla -yu.merla09@imperial.ac.uk Billy Wu -billy.wu@imperial.ac.uk Vladimir Yufit -v.yufit@imperial.ac.uk Nigel P. Brandon -n.brandon@imperial.ac.uk Ricardo F. Martinez-Botas -r.botas@imperial.ac.uk Gregory J. Offer -gregory.offer@imperial.ac.uk ABSTRACT Understanding and tracking battery degradation mechanisms and adapting its operation have become a necessity in order to enhance battery durability. A novel use of differential thermal voltammetry (DTV) is presented as an in-situ state-of-health (SOH) estimator for lithium-ion batteries.Accelerated ageing experiments were carried on 5Ah commercial lithium-ion polymer cells operated and stored at different temperature and loading conditions. The cells were analysed regularly with various existing in-situ diagnosis methods and the novel DTV technique to determine their SOH. The diagnosis results were used collectively to elaborate the degradation mechanisms inside the cells. The DTV spectra were decoupled into individual peaks, which each represent particular phases in the negative and positive electrode combined. The peak parameters were used to quantitatively analyse the battery SOH. 2A different cell of the same chemistry with unknown degradation history was then analysed to explore how the cell degraded. The DTV technique was able to diagnose the cell degradation without relying on supporting results from other methods nor previous cycling data. KEYWORDS:

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“…The performance y(t) can be the internal resistance (power fade) [5][6][7][8] or the capacity fade [9][10][11] with T the absolute temperature (in K), A the preexponential factor, E a the activation energy for the reaction (in eV), k the Boltzmann constant and f (t) 75 the time degradation function of y(t) considered.…”

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confidence: 99%

Eyring acceleration model for predicting calendar ageing of lithium-ion batteries

Redondo-Iglesias

Venet

Pélissier

2017

Journal of Energy Storage

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Modelling of lithium-ion batteries calendar ageing is often based on a semi-empirical approach by using, for example the Arrhenius acceleration model. Our approach is based on Eyring acceleration model, which is not widely used for electrochemical energy storage components. Parameter identification is typically performed without taking into account the state-of-charge (SoC) drifting. However, even in rest condition, battery cells' SoC drifts because of capacity losses (self-discharge and capacity fade). In this work we have taken into account the SoC drift during calendar ageing tests. For this, we considered available capacity (Ah) instead of SoC (%) as ageing factor. Then, the analytical solution of the problem leads to the use of the Lambert W function in the model formulation. Simulation results show that Lambert-Eyring model is more accurate and allows a reduction in the number of parameters to be identified.

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Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications

Cited by 139 publications

References 14 publications

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery

Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries

Eyring acceleration model for predicting calendar ageing of lithium-ion batteries

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