Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries

Predicting batteries' future degradation is essential for developing durable electric vehicles. The technical challenges arise from the absence of full battery degradation model and the inevitable local aging fluctuations in the uncontrolled environments. This paper proposes a base model-oriented gradient-correction particle filter (GC-PF) to predict aging trajectories of Lithium-ion batteries. Specifically, under the framework of typical particle filter, a gradient corrector is employed for each particle, resulting in the evolution of particle could follow the direction of gradient descent. This gradient corrector is also regulated by a base model. In this way, global information suggested by the base model is fully utilized, and the algorithm's sensitivity could be reduced accordingly. Further, according to the prediction deviations of base model, weighting factors between the local observations and base model can be updated adaptively. Four different battery datasets are used to extensively verify the proposed algorithm. Quantitatively, the RMSEs of GC-PF can be limited to 1.75%, which is 44% smaller than that of the conventional particle filter. In addition, the consistency of predictions when using different size of training data is also improved by 32%. Due to the pure data-driven nature, the proposed algorithm can also be extendable to other battery types

Run-to-Run Control for Active Balancing of Lithium Iron Phosphate Battery Packs

\ua9 1986-2012 IEEE. Lithium iron phosphate battery packs are widely employed for energy storage in electrified vehicles and power grids. However, their flat voltage curves rendering the weakly observable state of charge are a critical stumbling block for charge equalization management. This paper focuses on the real-time active balancing of series-connected lithium iron phosphate batteries. In the absence of accurate in situ state information in the voltage plateau, a balancing current ratio (BCR) based algorithm is proposed for battery balancing. Then, BCR-based and voltage-based algorithms are fused, responsible for the balancing task within and beyond the voltage plateau, respectively. The balancing process is formulated as a batch-based run-to-run control problem, as the first time in the research area of battery management. The control algorithm acts in two timescales, including timewise control within each batch run and batchwise control at the end of each batch. Hardware-in-the-loop experiments demonstrate that the proposed balancing algorithm is able to release 97.1% of the theoretical capacity and can improve the capacity utilization by 5.7% from its benchmarking algorithm. Furthermore, the proposed algorithm can be coded in C language with the binary code in 118 328 bytes only and, thus, is readily implementable in real time

Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method

Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life

Influence of lecithin cholesterol acyltransferase alteration during different pathophysiologic conditions: A 45 years bibliometrics analysis

Background: Lecithin cholesterol acyltransferase (LCAT) is an important enzyme responsible for free cholesterol (FC) esterification, which is critical for high density lipoprotein (HDL) maturation and the completion of the reverse cholesterol transport (RCT) process. Plasma LCAT activity and concentration showed various patterns under different physiological and pathological conditions. Research on LCAT has grown rapidly over the past 50 years, but there are no bibliometric studies summarizing this field as a whole. This study aimed to use the bibliometric analysis to demonstrate the trends in LCAT publications, thus offering a brief perspective with regard to future developments in this field.Methods: We used the Web of Science Core Collection to retrieve LCAT-related studies published from 1975 to 2020. The data were further analyzed in the number of studies, the journal which published the most LCAT-related studies, co-authorship network, co-country network, co-institute network, co-reference and the keywords burst by CiteSpace V 5.7.Results: 2584 publications contained 55,311 references were used to analyzed. The number of included articles fluctuated in each year. We found that Journal of lipid research published the most LCAT-related studies. Among all the authors who work on LCAT, they tend to collaborate with a relatively stable group of collaborators to generate several major authors clusters which Albers, J. published the most studies (n = 53). The United States of America contributed the greatest proportion (n = 1036) of LCAT-related studies. The LCAT-related studies have been focused on the vascular disease, lecithin-cholesterol acyltransferase reaction, phospholipid, cholesterol efflux, chronic kidney disease, milk fever, nephrotic syndrome, platelet-activating factor acetylhydrolase, reconstituted lpa-i, reverse cholesterol transport. Four main research frontiers in terms of burst strength for LCAT-related studies including “transgenic mice”, “oxidative stress”, “risk”, and “cholesterol metabolism “need more attention.Conclusion: This is the first study that demonstrated the trends and future development in LCAT publications. Further studies should focus on the accurate metabolic process of LCAT dependent or independent of RCT using metabolic marker tracking techniques. It was also well worth to further studying the possibility that LCAT may qualify as a biomarker for risk prediction and clinical treatment

Education in Process Systems Engineering: Why it matters more than ever and how it can be structured

This position paper is an outcome of discussions that took place at the third FIPSE Symposium in Rhodes, Greece, between June 20–22, 2016 (http://fi-in-pse.org). The FIPSE objective is to discuss open research challenges in topics of Process Systems Engineering (PSE). Here, we discuss the societal and industrial context in which systems thinking and Process Systems Engineering provide indispensable skills and tools for generating innovative solutions to complex problems. We further highlight the present and future challenges that require systems approaches and tools to address not only ‘grand’ challenges but any complex socio-technical challenge. The current state of Process Systems Engineering (PSE) education in the area of chemical and biochemical engineering is considered. We discuss approaches and content at both the unit learning level and at the curriculum level that will enhance the graduates’ capabilities to meet the future challenges they will be facing. PSE principles are important in their own right, but importantly they provide significant opportunities to aid the integration of learning in the basic and engineering sciences across the whole curriculum. This fact is crucial in curriculum design and implementation, such that our graduates benefit to the maximum extent from their learning