Annual Report on the Big Data of New Energy Vehicle in China (2021)

This open access book, based on static indicators and dynamic big data from local electric vehicles, is the first New-Energy Vehicles (NEVs) research report on the Big Data in China. Using the real-time big data collected by China's National Monitoring and Management Platform for NEVs, this book delves into the main annual technological progress of NEVs, the vehicle operating characteristics, it also anticipates the trend of NEVs industry. Various graphs&charts, detailed data this book offers will familiarize readers with the operation characteristics and practical application of China's NEVs industry and popularize the concept of automobile electrification. Besides, this book also makes an objective evaluation of the current situation and technological improvement of China's NEVs industry, presenting sensible suggestions for the development of the industry. This book is written for government staff, researchers, college staff, and technical staff of automobile and spare parts enterprises, which serves as an important reference for the decision-making of government departments and strategic decisions of automotive companies

Annual Report on the Big Data of New Energy Vehicle in China (2021)

This open access book, based on static indicators and dynamic big data from local electric vehicles, is the first New-Energy Vehicles (NEVs) research report on the Big Data in China. Using the real-time big data collected by China's National Monitoring and Management Platform for NEVs, this book delves into the main annual technological progress of NEVs, the vehicle operating characteristics, it also anticipates the trend of NEVs industry. Various graphs&charts, detailed data this book offers will familiarize readers with the operation characteristics and practical application of China's NEVs industry and popularize the concept of automobile electrification. Besides, this book also makes an objective evaluation of the current situation and technological improvement of China's NEVs industry, presenting sensible suggestions for the development of the industry. This book is written for government staff, researchers, college staff, and technical staff of automobile and spare parts enterprises, which serves as an important reference for the decision-making of government departments and strategic decisions of automotive companies

Electrochemical Characterization of Li 4

Li4Ti5O12/C composite was synthesized by starch-sol-assisted rheological phase method using inexpensive raw material starch as carbon coating precursor. The Li4Ti5O12/C powder was characterized using XRD, SEM, and TG techniques. The synthesized Li4Ti5O12 crystallites are cohesively covered by conductive carbon from starch sol which leads to increased conductivity, and the particle size of Li4Ti5O12/C is about 500 nm. The electrochemical performance of Li4Ti5O12/C was characterized by galvanostatic charge/discharge and EIS methods, and the results show that the Li4Ti5O12/C presents a high discharge capacity, high rate capability, and long cycle life. The capacity retention was at 87% (500 cycles at 1C) and 73.0% (2000 cycles at 20C) indicating promising high rate performance of Li4Ti5O12/C as anode material for lithium ion battery

Multi-Objective Thermal Optimization Based on Improved Analytical Thermal Models of a 30 kW IPT System for EVs

Thermal design is particularly important for high-power and compact inductive power transfer (IPT) systems having limited surface area for heat dissipation. This paper presents the thermal design and optimization of a 30 kW IPT system for electric vehicles. An improved analytical thermal model with high accuracy for liquid-cooled magnetic couplers was proposed by using thermal network method (TNM). It considers heating components as well as thermal interface materials. Then multi-objective thermal optimization procedure of the liquid-cooled magnetic coupler was conducted with the presented model. Tradeoffs among temperature rise, weight, and cost were discussed and an optimized solution was selected. The thermal FE models were established and compared with the thermal networks. Subsequently, the thermal performance of the system at different power levels and misaligned conditions was analyzed. The experimental setup based on Fiber Bragg grating sensors was built, and the prototypes were tested with an output power of around 28 kW. The error of stable temperature between the experiment and the prediction was less than 10% at the measurement points, verifying the thermal models. The proposed thermal models and optimization procedure accelerate the thermal design of IPT systems, towards higher power density.Multi-Objective Thermal Optimization Based on Improved Analytical Thermal Models of a 30 kW IPT System for EVsacceptedVersio

Offline and Online Blended Machine Learning for Lithium-Ion Battery Health State Estimation

This article proposes an adaptive state of health (SOH) estimation method for lithium-ion batteries using machine learning. Practical problems with feature extraction, cell inconsistency, and online implementability are specifically solved using a proposed individualized estimation scheme blending offline model migration with online ensemble learning. First, based on the data of pseudo-open-circuit voltage measured over the battery lifespan, a systematic comparison of different incremental capacity features is conducted to identify a suitable SOH indicator. Next, a pool of candidate models, composed of slope-bias correction (SBC) and radial basis function neural networks (RBFNNs), are trained offline. For online operation, the prediction errors due to cell inconsistency in the target new cell are then mitigated by a proposed modified random forest regression (mRFR) based ensemble learning process with high adaptability. The results show that compared to prevailing methods, the proposed SBC-RBFNN-mRFR-based scheme can achieve considerably improved SOH estimation accuracy (15%) with only a small amount of early-age data and online measurements are needed for practical operation. Furthermore, the applicability of the proposed SBC-RBFNN-mRFR algorithms to real-world operation is validated using measured data from electric vehicles, and it is shown that a 38% improvement in estimation accuracy can be achieved

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced b attery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted

Research on Quantitative Models of Electric Vehicle Charging Stations Based on Principle of Energy Equivalence

In order to adapt the matching and planning requirements of charging station in the electric vehicle (EV) marketization application, with related layout theories of the gas stations, a location model of charging stations is established based on electricity consumption along the roads among cities. And a quantitative model of charging stations is presented based on the conversion of oil sales in a certain area. Both are combining the principle based on energy consuming equivalence substitution in process of replacing traditional vehicles with EVs. Defined data are adopted in the example analysis of two numerical case models and analyze the influence on charging station layout and quantity from the factors like the proportion of vehicle types and the EV energy consumption at the same time. The results show that the quantitative model of charging stations is reasonable and feasible. The number of EVs and the energy consumption of EVs bring more significant impact on the number of charging stations than that of vehicle type proportion, which provides a basis for decision making for charging stations construction layout in reality

Nucleosomes Correlate with In Vivo Progression Pattern of De Novo Methylation of p16 CpG Islands in Human Gastric Carcinogenesis

BACKGROUND: The exact relationship between nucleosome positioning and methylation of CpG islands in human pathogenesis is unknown. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we characterized the nucleosome position within the p16 CpG island and established a seeding methylation-specific PCR (sMSP) assay based on bisulfite modification to enrich the p16 alleles containing methylated-CpG at the methylation "seeding" sites within its intron-1 in gastric carcinogenesis. The sMSP-positive rate in primary gastric carcinoma (GC) samples (36/40) was significantly higher than that observed in gastritis (19/45) or normal samples (7/13) (P<0.01). Extensive clone sequencing of these sMSP products showed that the density of methylated-CpGs in p16 CpG islands increased gradually along with the severity of pathological changes in gastric tissues. In gastritis lesions the methylation was frequently observed in the region corresponding to the exon-1 coding-nucleosome and the 5'UTR-nucleosome; the methylation was further extended to the region corresponding to the promoter-nucleosome in GC samples. Only few methylated-CpG sites were randomly detected within p16 CpG islands in normal tissues. The significantly inversed relationship between the p16 exon-1 methylation and its transcription was observed in GC samples. An exact p16 promoter-specific 83 bp-MSP assay confirms the result of sMSP (33/55 vs. 1/6, P<0.01). In addition, p16 methylation in chronic gastritis lesions significantly correlated with H. pylori infection; however, such correlation was not observed in GC specimens. CONCLUSIONS/SIGNIFICANCE: It was determined that de novo methylation was initiated in the coding region of p16 exon-1 in gastritis, then progressed to its 5'UTR, and ultimately to the proximal promoter in GCs. Nucleosomes may function as the basic extension/progression unit of de novo methylation of p16 CpG islands in vivo

Polycomb CBX7 Directly Controls Trimethylation of Histone H3 at Lysine 9 at the p16 Locus

BACKGROUND: H3K9 trimethylation (H3K9me3) and binding of PcG repressor complex-1 (PRC1) may play crucial roles in the epigenetic silencing of the p16 gene. However, the mechanism of the initiation of this trimethylation is unknown. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we found that upregulating the expression of PRC1 component Cbx7 in gastric cancer cell lines MGC803 and BGC823 led to significantly suppress the expression of genes within the p16-Arf-p15 locus. H3K9me3 formation was observed at the p16 promoter and Regulatory Domain (RD). CBX7 and SUV39H2 binding to these regions were also detectable in the CBX7-stably upregulated cells. CBX7-SUV39H2 complexes were observed within nucleus in bimolecular fluorescence complementation assay (BiFC). Mutations of the chromodomain or deletion of Pc-box abolished the CBX7-binding and H3K9me3 formation, and thus partially repressed the function of CBX7. SiRNA-knockdown of Suv39h2 blocked the repressive effect of CBX7 on p16 transcription. Moreover, we found that expression of CBX7 in gastric carcinoma tissues with p16 methylation was significantly lower than that in their corresponding normal tissues, which showed a negative correlation with transcription of p16 in gastric mucosa. CONCLUSION/SIGNIFICANCE: These results demonstrated for the first time, to our knowledge, that CBX7 could initiate H3K9me3 formation at the p16 promoter