Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg, Akhil; Su, Shaosen; Gao, Liang; Peng, Xiongbin; Baredar, Prashant

doi:10.1002/er.5096

Cited by 17 publications

(14 citation statements)

References 69 publications

(138 reference statements)

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“…F I G U R E 2 Arbin battery testing system: (A) a schematic diagram (B) experimental setup 42 [Colour figure can be viewed at 30 as charge period is more stable than discharge period in case of EVs. 28…”

Section: Battery Data Acquisition Processmentioning

confidence: 99%

Section: Experimental Battery Testing Setup and Data Acquisition Procmentioning

confidence: 99%

“…The data acquisition procedure is followed as per our already published work 42 . The battery signals such as voltage, current, charge capacity, and temperature are measured by Arbin testing system as shown in Figure 2 42 . Four batteries (detailed information of the battery is shown in Table 1) are selected to carry out the experiment and two best performing batteries have been considered in this study and named as cell 1 and cell 2.…”

Section: Experimental Battery Testing Setup and Data Acquisition Procmentioning

confidence: 99%

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Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

et al. 2020

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Summary Data‐driven modeling using measurable battery signals tends to provide robust battery capacity estimation without delving deep into electrochemical phenomenon inside the battery. Nowadays, with the advent of artificial intelligence, deep neural networks are playing crucial role in data modeling and analysis. In this article, models of three different families of network architectures such as feed‐forward neural network (FNN), convolutional neural network (CNN), and long short‐term memory neural network (LSTM) are proposed for battery capacity estimation. Measurements from a set of two rechargeable Li‐ion batteries are considered for the model performance evaluation. The battery capacity estimation by different models has been evaluated by considering the effect of certain parameters such as model complexity, sampling rate of battery measurable signals and type of battery measurable signals. With its ability to process time‐series data efficiently by memorizing long‐term dependencies, LSTM outperforms other model architectures in estimating battery capacity more accurately and flexibly with 4.69% and 19.16% decline in average test root mean square error (RMSE) as compared with FNN and CNN, respectively. Simpler architectures of LSTM and FNN are able to perform well as compared with CNN, which needs architecture with certain hidden layers to interpret the battery aging process. Moreover, investigations reveal that sparsely sampled battery signals help all the proposed models to learn the battery dynamics in a better way as compared to densely sampled battery signals which also entails for less complex model learning process. Further, among all battery measurable signals, battery temperature has relatively less weightage in estimating battery capacity.

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Section: Battery Data Acquisition Processmentioning

confidence: 99%

Section: Experimental Battery Testing Setup and Data Acquisition Procmentioning

confidence: 99%

Section: Experimental Battery Testing Setup and Data Acquisition Procmentioning

confidence: 99%

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Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

et al. 2020

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“…Thus, to provide a solid basis for retired battery clustering, the residual energy detection system is worthy of further study. 23,24 In this procedure, a key technology is the battery aging model construction. The existed modeling techniques include model-based methods…”

Section: Waste Battery Classification System Based On Residual Energy Detectionmentioning

confidence: 99%

Battery pack recycling challenges for the year 2030: Recommended solutions based on intelligent robotics for safe and efficient disassembly, residual energy detection, and secondary utilization

et al. 2020

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With the increasing use of batteries, battery recycling would become a considerable problem in the next decade. However, the current recycling technologies are still on the stage of research and development. A significant challenge in the traditional recycling method is that the recovery procedure relies heavily on manual work. Therefore, it is necessary to develop an intelligent, automatic or semi-automatic framework for rapid disassembly and efficient recovery of retired batteries. In this study, the key research problems during the battery recycling process were identified first. The main recycling process was divided into three parts: automatic disassemble process, residual energy detection, and second utilization as well as chemical recycling. Based on the above research gaps, a qualitative framework of UR5 robots for safe and fast battery recycling, residual energy detection, and secondary utilization of retired batteries was proposed. The framework includes a battery position and shape measurement system based on machine vision, an automatic battery removal system based on UR5 industrial robot, a battery residual energy detection, and classification system. Furthermore, a real case study of battery pack recycling was carried out based on manual work and automatic robot work. The results show that the proposed recycling framework can work efficiently compared with the traditional recycling method. Moreover, the future perspectives of battery recycling are summarized. K E Y W O R D S conservation of materials, disassembly, efficient recycling, environmental benefits, intelligent robotics, recovery 1 | INTRODUCTION At present, China ranks among the world's largest producers and consumers of lithium batteries. The annual battery consumption reaches 8 billion. 1 If the waste lithium batteries are not processed and recycled systematically, it will seriously waste resources, pollute the environment, and endanger human health. It can be seen that the recycling and reuse of waste batteries has a broad market. Due to the accelerated replacement of consumer

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“…Significant efforts have been made to study the effect of the SEI layer formation on the deterioration of electrochemical performance in Li-ion batteries due to reduction in the cyclable lithium inventory and increase in the internal resistance. [37][38][39][40][41] However, our studies focus on the role of the SEI layer on the mechanical stability of electrode particles, which has rarely been studied before. The SEI layer growth changes the level of mechanical constraints to the active particles, which can affect the particle fracture probability.…”

Section: Introductionmentioning

confidence: 99%

Role of SEI layer growth in fracture probability in lithium‐ion battery electrodes

Ali

Iqbal

Lee

2020

Intl J of Energy Research

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Understanding of degradation mechanisms in batteries is essential for the widespread use of eco-friendly vehicles. Degradation mechanisms affect battery performance not only individually but also in a coupled manner. Solid electrolyte interface (SEI) formation deteriorates battery capacity through consuming available lithium ions. On the other hand, as the SEI layer grows over multiple cycles, the level of mechanical constraints is changed, which can affect the fracture behavior of the active particles. We investigate the effect of the SEI layer growth on the fracture probability of the electrode particles. The simulations show that as the SEI layer grows, tensile stress inside the active particles turns into compressive stress, reducing the probability of particle fracture. Once the SEI layer is fractured, the particle fracture is sequentially more likely to happen because the SEI constraint is removed. The study emphasizes that the stability of SEI layers is important because it helps in alleviating electrochemical performance fade as well as mechanical failure probability. In addition, the SEI layer on small particles tends to be more fractured than that on large particles, suggesting that the particle size uniformity is essential for reducing the fracture probability of the SEI layers at the electrode.

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Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Cited by 17 publications

References 69 publications

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

Battery pack recycling challenges for the year 2030: Recommended solutions based on intelligent robotics for safe and efficient disassembly, residual energy detection, and secondary utilization

Role of SEI layer growth in fracture probability in lithium‐ion battery electrodes

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