Lifetime of self-reconfigurable batteries compared with conventional batteries

Bouchhima, Nejmeddine; Gossen, Matthias; Schulte, Sascha; Birke, Kai Peter

doi:10.1016/j.est.2017.11.014

Cited by 33 publications

(18 citation statements)

References 21 publications

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“…While providing the mentioned benefits, battery performance is impaired by battery degradation through chronological and utilisation causes. For example, Bouchhima et al [2] explain that the battery health state describe the current EV performance [2]. This health state is influenced by permanent and inevitable degradation [4,16].…”

Section: General Battery Degradationmentioning

confidence: 99%

“…This is because after the battery health drops below 80% SoH, the capacity fades rapidly [29]. Thus, the End-of-Life (EOL) of EV batteries is estimated to be 80% SoH [2] i.a. The small lifetime range with regards to the SoH classification therefore needs to be preserved.…”

Section: General Battery Degradationmentioning

confidence: 99%

“…Besides the general context of battery degradation, there exist static recommendations with give advice in order to reduce the effects of cyclic and calendaric ageing of Li-ion batteries. On one hand cyclic ageing takes place because charge throughput into and out of the battery during cycles of driving and charging [2,22,26] at typical EV use. On the other, calendaric ageing is observed over the course of time and the powered off state of the EV.…”

Section: Static Recommendationsmentioning

confidence: 99%

“…Also, the DoD (Depth of Discharge) is often used as measure, which is the difference between 100% and SoC at a certain point in time [20]. In general, it should be avoided to frequently discharge the battery to a high DoD ( [2] i.a.) in order to prolong battery lifetime.…”

Section: Static Recommendationsmentioning

confidence: 99%

“…in order to prolong battery lifetime. Furthermore, the SoC difference during charging or discharging should be kept low, as this also accelerates battery degradation [2,16,18].…”

Section: Static Recommendationsmentioning

confidence: 99%

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Dynamic EV Battery Health Recommendations

Eider

Berl

2018

Proceedings of the Ninth International Conference on Future Energy Systems

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Prolonging the lifetime of batteries in Electric Vehicles (EVs) becomes a more and more important issue for private users and fleet operators. In addition to the environmental point of view, a better battery health results in less cost, higher battery capacities and higher performance. To achieve this, the EV drivers or the fleet operators need to get proper information, which kind of actions will increase or decrease the batteries health. To this point, various tips and recommendations exist distributed over literature. Unfortunately, those kind of recommendations are hard to follow in the day-today routine. This paper suggests so called dynamic recommendations for battery health that are able to advise the user in specific situations with respect to battery use. Recommendations from literature are broken down into a list, which can be automatically computed. Recommendations will then be dynamically created in the current context of the EV and displayed to the user just in time. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Activity centered design; • Applied computing → Physics; • Information systems → Data analytics;

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Section: General Battery Degradationmentioning

confidence: 99%

Section: General Battery Degradationmentioning

confidence: 99%

Section: Static Recommendationsmentioning

confidence: 99%

Section: Static Recommendationsmentioning

confidence: 99%

“…in order to prolong battery lifetime. Furthermore, the SoC difference during charging or discharging should be kept low, as this also accelerates battery degradation [2,16,18].…”

Section: Static Recommendationsmentioning

confidence: 99%

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Dynamic EV Battery Health Recommendations

Eider

Berl

2018

Proceedings of the Ninth International Conference on Future Energy Systems

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Comparison of dU/dQ, Voltage Decay, and Float Currents via Temperature Ramps and Steps in Li‐ion Batteries

Azzam,

Ehrensberger,

Endisch

et al. 2024

Batteries & Supercaps

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In this study, the effect of temperature changes on the voltage decay and current behavior of lithium‐ion cells is investigated, focusing on a comparison between open‐circuit voltage (OCV) measurements and float current [[EQUATION]] measurements. Using our self‐developed advanced Floater system, the voltage decay rates [[EQUATION]] from OCV and float current measurements for three different cell types are assessed. Temperature ramps and steps, ranging from 5°C to 50°C, are applied to capture the impact of entropic effects and aging mechanisms. Both methods effectively capture aging dynamics, showing strong agreement between ramp and step measurements. Deviations arise only in cases of strong entropy effects due to differences in measurement strategies. The findings confirm that float currents do not introduce additional aging beyond that captured by OCV measurements. The relationship between OCV and float current is governed by differential capacity [[EQUATION]], which varies with cell voltage and temperature. Furthermore, strong deviations from classical differential voltage analysis but high agreement with local pulse measurements are observed, especially at low depths of discharge. This can be explained by the hysteresis effect of graphite. These findings highlight the benefits of high‐precision float current measurements in aging studies, particularly in contrast to simpler OCV methods.

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Solar Hybrid Systems and Energy Storage Systems

Aktaş

Kırçiçek

2021

Solar Hybrid Systems

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Lifetime of self-reconfigurable batteries compared with conventional batteries

Cited by 33 publications

References 21 publications

Dynamic EV Battery Health Recommendations

Dynamic EV Battery Health Recommendations

Comparison of dU/dQ, Voltage Decay, and Float Currents via Temperature Ramps and Steps in Li‐ion Batteries

Solar Hybrid Systems and Energy Storage Systems

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