scholarly journals A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Luciano Sánchez ◽  
Inés Couso ◽  
José Otero ◽  
Yuviny Echevarría ◽  
David Anseán
Keyword(s):  
Knowledge Bases ◽  
Rechargeable Batteries ◽  
Virtual Sensor ◽  
Model Based ◽  
Fuzzy Knowledge Bases ◽  
Current Voltage ◽  
Vehicle Systems ◽  
Li Ion ◽  
Definition Of ◽  
Vehicle Sensors

A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

scholarly journals Review of Studies on Older Drivers’ Behavior and Stress—Methods, Results, and Outlook

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3503
Author(s):  
Yanning Zhao ◽  
Toshiyuki Yamamoto
Keyword(s):  
Age Groups ◽  
Older Drivers ◽  
Driving Simulators ◽  
Driving Experience ◽  
Older Driver ◽  
Vehicle Data ◽  
Vehicle Systems ◽  
Questionnaire Surveys ◽  
Definition Of ◽  
Short Time

This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers or considers participants according to various age groups. Nevertheless, the definition of ‘older driver’ varies not only across studies but also across different government reports. Although questionnaire surveys are widely used to affordably obtain massive data in a short time, they lack objectivity. In contrast, biomedical information can increase the reliability of a driving stress assessment when collected in environments such as driving simulators and on-road experiments. Various studies determined that driving behavior and stress remain stable regardless of age, whereas others reported degradation of driving abilities and increased driving stress among older drivers. Instead of age, many researchers recommended considering other influencing factors, such as gender, living area, and driving experience. To mitigate bias in findings, this literature review suggests a hybrid method by applying surveys and collecting on-road/in-vehicle data.

Online Estimation of Lithium-Ion Battery Capacity Using Deep Convolutional Neural Networks

2018 ◽  
Author(s):  
Sheng Shen ◽  
M. K. Sadoughi ◽  
Xiangyi Chen ◽  
Mingyi Hong ◽  
Chao Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Rechargeable Batteries ◽  
Li Ion Battery ◽  
Deep Convolutional Neural Networks ◽  
Battery Capacity ◽  
Li Ion

Over the past two decades, safety and reliability of lithium-ion (Li-ion) rechargeable batteries have been receiving a considerable amount of attention from both industry and academia. To guarantee safe and reliable operation of a Li-ion battery pack and build failure resilience in the pack, battery management systems (BMSs) should possess the capability to monitor, in real time, the state of health (SOH) of the individual cells in the pack. This paper presents a deep learning method, named deep convolutional neural networks, for cell-level SOH assessment based on the capacity, voltage, and current measurements during a charge cycle. The unique features of deep convolutional neural networks include the local connectivity and shared weights, which enable the model to estimate battery capacity accurately using the measurements during charge. To our knowledge, this is the first attempt to apply deep learning to online SOH assessment of Li-ion battery. 10-year daily cycling data from implantable Li-ion cells are used to verify the performance of the proposed method. Compared with traditional machine learning methods such as relevance vector machine and shallow neural networks, the proposed method is demonstrated to produce higher accuracy and robustness in capacity estimation.

scholarly journals Tuning Silicon Nanorods for Anodes of Li-Ion Rechargeable Batteries

2019 ◽  
Vol 33 (27) ◽  
pp. 35-43 ◽  
Author(s):  
Ming Au ◽  
Brenda Garcia-Diaz ◽  
Thad Adams ◽  
Yuping He ◽  
Yiping Zhao ◽  
...  
Keyword(s):  
Rechargeable Batteries ◽  
Li Ion

scholarly journals A Fast Approach to Obtain Layered Transition-Metal Cathode Material for Rechargeable Batteries

Batteries ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 4
Author(s):  
Shofirul Sholikhatun Nisa ◽  
Mintarsih Rahmawati ◽  
Cornelius Satria Yudha ◽  
Hanida Nilasary ◽  
Hartoto Nursukatmo ◽  
...  
Keyword(s):  
Transition Metal ◽  
Cathode Material ◽  
Rechargeable Batteries ◽  
Material Behavior ◽  
Heating Process ◽  
Battery Materials ◽  
High Storage Capacity ◽  
Li Ion ◽  
Layered Transition Metal ◽  
High Storage

Li-ion batteries as a support for future transportation have the advantages of high storage capacity, a long life cycle, and the fact that they are less dangerous than current battery materials. Li-ion battery components, especially the cathode, are the intercalation places for lithium, which plays an important role in battery performance. This study aims to obtain the LiNixMnyCozO2 (NMC) cathode material using a simple flash coprecipitation method. As precipitation agents and pH regulators, oxalic acid and ammonia are widely available and inexpensive. The composition of the NMC mole ratio was varied, with values of 333, 424, 442, 523, 532, 622, and 811. As a comprehensive study of NMC, lithium transition-metal oxide (LMO, LCO, and LNO) is also provided. The crystal structure, functional groups, morphology, elemental composition and material behavior of the particles were all investigated during the heating process. The galvanostatic charge–discharge analysis was tested with cylindrical cells and using mesocarbon microbeads/graphite as the anode. Cells were tested at 2.7–4.25 V at 0.5 C. Based on the analysis results, NMC with a mole ratio of 622 showed the best characteristicd and electrochemical performance. After 100 cycles, the discharged capacity reaches 153.60 mAh/g with 70.9% capacity retention.

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