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Numerical Heat Transfer And Fluid Flow

Lithium-ion batteries decay every time as it is used. Aging-induced degradation is unlikely to be eliminated. The aging mechanisms of lithium-ion batteries are manifold and complicated which are strongly linked to many interactive factors, such as battery types, electrochemical reaction stages, and operating conditions. In this paper, we systematically summarize mechanisms and diagnosis of lithium-ion battery aging. Regarding the aging mechanism, effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode, and other battery structures. The influence of different external factors on the aging mechanism is explained, in which temperature can exert the greatest impact compared to other external factors. As for aging diagnosis, three widely-used methods are discussed: disassembly-based post-mortem analysis, curve-based analysis, and model-based analysis. Generally, the post-mortem analysis is employed for cross-validation while the curve-based analysis and the model-based analysis provide quantitative analysis. The challenges in the use of quantitative diagnosis and on-board diagnosis on battery aging are also discussed, based on which insights are provided for developing online battery aging diagnosis and battery health management in the next generation of intelligent battery management systems (BMSs).

Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives

Depleting energy resources have become the driving force for their conservation. Increasing the system efficiencies is one method by which sustainability of energy may be ensured, for which miniaturization has successfully provided solutions. Miniature heat exchangers, owing to their high thermal performance, have the potential to provide energy efficient systems. In addition, their characteristics of compactness, small size and lesser weight have attracted widespread applications. Various works on micro- and minichannel heat exchangers as heat sinks and heat exchangers have been reviewed in this paper. Currently employed fabrication techniques and different applications have been summarized. An overview of the single-phase thermo-hydraulic studies in micro- and minichannel heat sinks has been presented. Literatures related co-current, counter-current and cross-current micro- and minichannel heat exchangers have been discussed. Finally, the persisting lacunae of this technology drawn from the review have been pointed out.

Review of micro- and mini-channel heat sinks and heat exchangers for single phase fluids

https://iopscience.iop.org/article/10.1149/2.0281914jes/pdf

Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries

The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm

Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Prediction of battery calendar ageing is a key but challenging issue in the development of durable electric vehicles. This paper simultaneously evaluates three mainstream types of modelling techniques for calendar ageing prediction of Lithium-ion (Li-ion) batteries. They are the pseudo two dimensional (P2D)-based electrochemical model, Arrhenius law-based semi-empirical model, and Gaussian process regression (GPR)-based data-driven model. Specifically, both the electrochemical and semi-empirical models are consciously developed or selected from the state-of-the-art modelling literature. For the data-driven model, due to the limited research in the existing publications, a machine learning-enabled GPR model is derived and applied for calendar ageing prediction. An experimental setup is developed to load the commercial Panasonic NCR18650BD batteries and to collect the experimental calendar ageing data under different storage temperature and SOC levels over 435 days. Based upon this well-rounded database, each model is well trained through using its corresponding training solution. Then the prediction performances of these models are studied and evaluated in terms of the model accuracy, generalization ability and uncertainty management. Both the challenges and future prospects of each model type are highlighted to assist the industrial and academic research communities, thus boosting the progress of designing advanced modelling techniques in battery calendar ageing prediction domain.

T.R. Ashwin

Papers

Capacity fade modelling of lithium-ion battery under cyclic loading conditions

An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries

CFD analysis of high frequency miniature pulse tube refrigerators for space applications with thermal non-equilibrium model

Electrochemical modelling of Li-ion battery pack with constant voltage cycling

The impact of high-frequency-high-current perturbations on film formation at the negative electrode-electrolyte interface