Lifetime Test Design for Second-Use Electric Vehicle Batteries in Residential Applications

Li, He; Alsolami, Mohammed; Yang, Shaobing; Alsmadi, Yazan M.; Wang, Jin

doi:10.1109/tste.2017.2707565

Cited by 30 publications

(11 citation statements)

References 28 publications

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“…The experiments were carried out at room temperature, and the experimental conditions of the seconduse batteries are shown in Table 2. The retired batteries are mostly used under a low current rate during second use [1], so the maximu m discharge current rate we selected is 1C. The charging current rates we chose include 0.5C and 1C.…”

Section: Figure 2 Schematic Of the Test Benchmentioning

confidence: 99%

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Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

2021

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Section: Figure 2 Schematic Of the Test Benchmentioning

confidence: 99%

“…As more electric vehicles (EVs) have appeared on roads, the disposal of EV batteries has become an increasing concern [1]. When the energy storage capacity of an EV battery declines to 80%, the battery is no longer suitable for EVs and must be replaced [2].…”

Section: Introductionmentioning

confidence: 99%

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

2021

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“…Apart from their costly production, batteries have a limited lifetime. According to Li et al (2017), by 2020 nearly one million electric vehicle battery packs will be "retired" in the USA alone, meaning that they cannot be used in electric vehicles anymore. This happens when (i) the battery only provides less than 80% of the original capacity and maximum power, or (ii) there are functional failures occurring.…”

Section: State Of the Artmentioning

confidence: 99%

Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

Perger

Auer

2020

Energy Efficiency

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In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

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“…Lithium-ion batteries for electric vehicles (EVs) begin to degrade much faster after the capacity drops below about 80% [1][2][3], and manufacturers generally recommended that they be replaced at this point. However, these batteries are still adequate for other applications such as energy storage for the electric grid, and it is expected that vast numbers of them will become available as EV usage increases [4][5][6][7][8][9]. Figure 1 is an example graph showing the EV and second life (SL) regions of operation.…”

Section: Introductionmentioning

confidence: 99%

Bilevel vs. Passive Equalizers for Second Life EV Batteries

2021

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Once lithium-ion batteries degrade to below about 80% of their original capacity, they are no longer considered satisfactory for electric vehicles (EVs), but they are still adequate for second-life energy storage applications. However, once this level is reached, capacity fade increases at a much faster rate, and the spread between the cell capacities becomes much wider. If the passive equalizer (PEQ) from the EV is still used, battery capacity remains equal to that of the worst cell in the stack, just like it was in the EV. Unfortunately, the worst cell eventually becomes much weaker than the cell average, and the other cells are not fully utilized. If operated while the battery is in use, an active equalizer (AEQ) can increase the battery capacity to a much higher value close to the cell average, but AEQs are much more expensive and are not considered cost effective. However, it can be shown that the bilevel equalizer (BEQ), a PEQ/AEQ hybrid, also can provide a capacity very close to the cell average and at a much lower cost than an AEQ.

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Lifetime Test Design for Second-Use Electric Vehicle Batteries in Residential Applications

Cited by 30 publications

References 28 publications

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

Bilevel vs. Passive Equalizers for Second Life EV Batteries

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