Aging of lithium-ion batteries for electric vehicles

Danzer, Michael A.; Liebau, Verena; Maglia, Filippo

doi:10.1016/b978-1-78242-377-5.00014-5

Cited by 37 publications

(20 citation statements)

References 65 publications

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“…Detrimental effects may also descend from other processes, such as interaction between binder polymer with active and conductive particles, current collector corrosion and plating, gas formation within the cell [7,3,37].…”

Section: Microscopic Phenomena and Their Modelingmentioning

confidence: 99%

“…Being the latter characterized by limited lifetime, battery aging was not a central topic. The expected use of LIBs for highpower and high-capacity demanding systems, as EVs, [3] and for storage systems for renewable energy sources makes contained capacity fading and power loss nowadays priorities for the world-wide research community. Whereas experimental studies are the backbones of batteries investigation, modeling can provide fundamental contributions, particularly in tailoring material performances and degradation.…”

Section: Introductionmentioning

confidence: 99%

“…The main mechanisms of aging can be categorized in four groups, namely: surface film formation (solid electrolyte interphase (SEI), Lithium plating), bulk changes (phase segregation), mechanical effects due to lithiation (fracturing, dissipation, grinding), and parasitic reactions (corrosion, binder degradation). An up to date review of the literature on aging of Lithium-ion batteries for electric vehicles can be found in [3], with special emphasis on physicochemical transformations. Recent publications on aging and degradation accentuated the role of mechanical detrimental effects.…”

Section: Introductionmentioning

confidence: 99%

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Computational modeling of Li-ion batteries

2016

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This review focuses on energy storage materials modeling, with particular emphasis on Li-ion batteries. Theoretical and computational analyses not only provide a better understanding of the intimate behavior of actual batteries under operational and extreme conditions, but they may tailor new materials and shape new architectures in a complementary way to experimental approaches. Modeling can therefore play a very valuable role in the design and lifetime prediction of energy storage materials and devices. Batteries are inherently multi-scale, in space and time. The macro-structural characteristic lengths (the thickness of a single cell, for instance) are order of magnitudes larger than the particles that form the microstructure of the porous electrodes, which in turn are scale-separated from interface layers at which atomistic intercalations occur. Multi-physics modeling concepts, methodologies, and simulations at different scales, as well as scale transition strategies proposed in the recent literature are here revised. Finally, computational challenges toward the next generation of Li-ion batteries are discussed.

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Section: Microscopic Phenomena and Their Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computational modeling of Li-ion batteries

2016

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“…The internal resistance and capacity of lithium batteries and supercapacitors varies depending on a number of factors, such as state of charge (SOC), value and direction of load current, temperature, and state of health (SOH) [14][15][16][17][18]. These changes, varying from 70% to 200% of rated values given by manufacturers [14], [19][20][21][22] have minor influence on trams energy consumption comparison.…”

Section: Ess Models and Simulationmentioning

confidence: 99%

Energy storage system in traction vehicle

Wieczorek

Lewandowski

2018

MATEC Web Conf.

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The paper compares three different types of energy storage system (ESS) in a tramway. It was assumed that the tram has to travel without catenary for 5 km. Two homogeneous energy storage systems were designed to provide energy for the ride: the first made of lithium-ion batteries and the second made of supercapacitors. The third solution is a hybrid ESS (HESS) composed of both of the aforementioned technologies. The comparison was made in terms of weight, price and power consumption when driving with and without a catenary. Using a well-fitted energy management strategy, 50% lower HESS mass was achieved compared to the homogeneous ESSes. The estimated price of the HESS is 29% lower than the battery ESS and 59% from the supercapacitor ESS. A comparison of the energy consumption of the tram with and without the ESS is also presented, depending on the amount of energy absorbed by other vehicles on the same power section. It follows that the use of the ESS allows for significant energy savings for driving with the catenary.

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“…However, battery fade is a complex phenomenon provoked by environmental conditions and utilization modes [2]. In an attempt to capture the real-world utilization impacts on battery degradation, OEMs perform, at the first step of an automotive development cycle for lithium-ion batteries as shown in Figure 1, ageing tests on single cells by applying realistic duty cycles on them [3,4]. However, these duty cycles are vague approximations and do not account for the daily variation and unpredictability of such a system [5].…”

Section: Introductionmentioning

confidence: 99%

Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems

Nuhic

Bergdolt

Spier

et al. 2018

WEVJ

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Today, fleet management systems with battery health monitoring capabilities are in the focus more than ever. This paper addresses the development of a novel battery health monitoring algorithm with a degradation prognosis feasibility particularly adapted for usage in fleet management systems. Moreover, the chosen degradation prognosis approach adapts itself continuously on varying environmental conditions or utilization modes by identifying the impact factors which lead to a certain degradation trend. Such findings, when accessible with a fleet management system, offer various possibilities for fleet analysis techniques e.g., to identify an imminent battery failure.

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Aging of lithium-ion batteries for electric vehicles

Cited by 37 publications

References 65 publications

Computational modeling of Li-ion batteries

Computational modeling of Li-ion batteries

Energy storage system in traction vehicle

Battery Health Monitoring and Degradation Prognosis in Fleet Management Systems

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