Functional LiTaO3 filler with tandem conductivity and ferroelectricity for PVDF-based composite solid-state electrolyte

Composite solid-state electrolytes have received significant attention due to their combined advantages as inorganic and polymer electrolytes. However, conventional ceramic fillers offer limited ion conductivity enhancement for composite solid-state electrolytes due to the space-charge layer between the polymer matrix and ceramic phase. In this study, we develop a ferroelectric ceramic ion conductor (LiTaO3) as a functional filler to simultaneously alleviate the space-charge layer and provide an extra Li+ transport pathway. The obtained composite solid-state electrolyte comprising LiTaO3 filler and poly (vinylidene difluoride) matrix (P-LTO15) achieves an ionic conductivity of 4.90 × 10−4 S cm−1 and a Li+ transference number of 0.45. The polarized ferroelectric LiTaO3 creates a uniform electric field and promotes homogenous Li plating/stripping, providing the Li symmetrical batteries with an ultrastable cycle life for 4000 h at 0.1 mA cm−2 and a low polarization overpotential (~50 mV). Furthermore, the solid-state NCM811/P-LTO15/Li full batteries achieve an ultralong cycling performance (1400 cycles) at 1 C and a high discharge capacity of 102.1 mAh g−1 at 5 C. This work sheds light on the design of functional ceramic fillers for composite solid-state electrolytes to effectively enhance ion conductivity and battery performance

Identification of BC005512 as a DNA Damage Responsive Murine Endogenous Retrovirus of GLN Family Involved in Cell Growth Regulation

Genotoxicity assessment is of great significance in drug safety evaluation, and microarray is a useful tool widely used to identify genotoxic stress responsive genes. In the present work, by using oligonucleotide microarray in an in vivo model, we identified an unknown gene BC005512 (abbreviated as BC, official full name: cDNA sequence BC005512), whose expression in mouse liver was specifically induced by seven well-known genotoxins (GTXs), but not by non-genotoxins (NGTXs). Bioinformatics revealed that BC was a member of the GLN family of murine endogenous retrovirus (ERV). However, the relationship to genotoxicity and the cellular function of GLN are largely unknown. Using NIH/3T3 cells as an in vitro model system and quantitative real-time PCR, BC expression was specifically induced by another seven GTXs, covering diverse genotoxicity mechanisms. Additionally, dose-response and linear regression analysis showed that expression level of BC in NIH/3T3 cells strongly correlated with DNA damage, measured using the alkaline comet assay,. While in p53 deficient L5178Y cells, GTXs could not induce BC expression. Further functional studies using RNA interference revealed that down-regulation of BC expression induced G1/S phase arrest, inhibited cell proliferation and thus suppressed cell growth in NIH/3T3 cells. Together, our results provide the first evidence that BC005512, a member from GLN family of murine ERV, was responsive to DNA damage and involved in cell growth regulation. These findings could be of great value in genotoxicity predictions and contribute to a deeper understanding of GLN biological functions

Circuit grid approach for mobile robot path planning

The path planning is still a challenging problem even after the past 30 years' research. A global off-line path planning algorithm based on circuit grid approach is presented in this dissertation. This approach makes it easy to find the optimal feasible path. The generated path function can combine various environmental conditions such as hazardous, jamming and broad, etc. It overcomes the shortcoming of local optimum traps in some path planners. Also, this algorithm is always converging if a feasible path does exist.Master of Science (Computer Control and Automation

Lithium-ion battery state-of-charge estimation based on a dual extended Kalman filter and BPNN correction

It is challenging for a battery management system to estimate the State-Of-Charge (SOC) of batteries. A novel model-based method, using a Dual Extended Kalman Filtering algorithm (DEKF) and Back Propagation Neural Network (BPNN), is proposed to estimate and correct lithium-ion batteries. The results of acceptable SOC estimation are achieved using the DEKF to estimate the battery SOC and simultaneously update model parameters online, while the SOC estimation error is in real-time predicted by the trained BPNN. To further reduce the SOC estimation error, the SOC estimated by the DEKF is corrected by adding the predicted estimation error. The SOC estimation results between the original DEKF and BPNN-based updated DEKF methods under the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), the Beijing Dynamic Stress Test (BJDST) and the US06 Highway Driving Schedule are compared. Experimental results show that the SOC error reduces considerably after correcting the estimated SOC. The corrected SOC Root-Mean Square Errors (RMSEs) decrease by an average of seven times compared with the case of no correction. The constant current discharge test verifies the generality and robustness of the proposed method. The modification to the SOC estimation results using ordinary EKF under the above four sophisticated dynamic tests verifies the effectiveness of the proposed method

Effects of Fertilizer Level and Intercropping Planting Pattern with Corn on the Yield-Related Traits and Insect Community of Soybean

Intercropping of corn and soybean is widely practiced in agricultural production. However, few studies have investigated the effect of intercropping and fertilizer reduction on soybean yield. In the present study, corn and soybean were interplanted in 2:2, 2:3 and 2:4 ratios. Two fertilizer levels (normal: 600 kg/ha VS. reduced: 375 kg/ha) were set. The effects of fertilizer levels and intercropping planting patterns on the growth and yield of intercropping soybeans were studied based on the changes in enzyme activities related to nitrogen metabolism and insect community in the field. The results show that fertilizer reduction significantly reduced the biomass, 100-seed weight and yield of soybean. Intercropping also reduced these yield-related traits; a decreasing trend was more obvious with a decrease in soybean ratio. Intercropping had greater effect on soybean plant biomass, 100-seed weight and yield than fertilizer reduction. Reduction in fertilizer reduced the activities of nitrogen-metabolism-related enzymes in soybean. In addition to increased NR (nitrate reductase) enzyme activity in R5, intercropping planting pattern also had negative effect on the activities of nitrogen-metabolism-related enzymes in soybean. Reduced fertilizer only significantly reduced the Pielou evenness index. Reduced fertilizer application was beneficial with respect to the outbreak of greenhouse whitefly. However, an intercropping planting pattern can significantly increase the number of species, as well as the Shannon–Wiener diversity index and the Pielou evenness index of the insect community, and significantly reduce the Simpson dominance index and the population of the important pest, green leafhopper. In conclusion, C2S4 (two corn rows with four rows of soybean) is a scientific intercropping planting pattern that can reduce the occurrence of pests through ecological regulation and does not significantly reduce the activity of enzymes-related to nitrogen metabolism in most cases, ensuring soybean yield

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the Particle Swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge–discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters

Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter

Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance R0 of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when R0 is inaccurate

Effects of Corn Intercropping with Soybean/Peanut/Millet on the Biomass and Yield of Corn under Fertilizer Reduction

Corn (Zea mays L.) is one of the key grain crops in China. In fields, the two crops of soybean (Glycine max L.) and peanut (Arachis hypogaea L.), which have nitrogen-fixing capacity (NFC), are generally used to intercrop with corn to improve plant physiology and production ability of corn even under fertilizer reduction. To explore a more scientific and reasonable way to plant corn, and simultaneously reduce the use of chemical fertilizers and pesticides, the impacts of corn intercropping with two NFC crops (including soybean and peanut) and the a non-NFC crop (i.e., millet (Setaria italica)) through five planting patterns, including three intercropping patterns (2 corn rows to 2, 3, and 4 NFC-crop rows or 2, 4, and 6 millet rows) and two sole crop patterns of corn and soybean, peanut, or millet under normal (600 kg/ha) and reduced (375 kg/ha) levels of NPK (N:P2O5:K2O = 15:15:15) fertilization levels on the activity of N-metabolism-related enzymes in corn rhizosphere soil and corn leaves, and plant biomass and yield of corn were researched in this study. The results showed that fertilizer reduction significantly decreased the plant biomass and grain yield of the sole crop corn. The intercropping type and planting pattern both had significant effects on the activities of N-metabolism-related enzyme of soil alkaline protease (S-ALPT), and glutamine oxoglutarate aminotransferase (GOGAT), glutamate synthetase (GS), and nitrate reductase (NR) in the leaves of corn plants. The intercropping type of corn with soybean through the planting pattern of 2 corn rows to 4 soybean rows significantly improved the activities of N-metabolism-related enzymes in soil and corn leaves even under the fertilizer reduction. The intercropping pattern of corn-soybean was the most beneficial to increase the total nitrogen content in soil and corn leaves. In addition, the intercropping significantly increased the soil microbial diversity under normal fertilizer. Furthermore, fertilizer reduction significantly increased soil microbial diversity of the corn sole crop. Therefore, it is concluded that for corn in intercropping systems, the best and the worst companion crop were, respectively, soybean and millet

Lithium Battery SOC Estimation Based on Multi-Innovation Unscented and Fractional Order Square Root Cubature Kalman Filter

Accurate state-of-charge (SOC) estimation of lithium batteries is of great significance for electric vehicles. In this paper, a combined estimation method of multi-innovation unscented Kalman filter (MIUKF) and fractional order square root cubature Kalman filter (FSRCKF) for lithium batteries is proposed. Firstly, the adaptive genetic algorithm (AGA) is applied to carry out offline parameter identification for the fractional order model (FOM) of a lithium battery under the Dynamic Stress Test (DST). Then, battery SOC is estimated by FSRCKF, while the Ohm internal resistance R0 of the fractional order battery model is estimated and updated by MIUKF in real time. The results show that MIUKF-FSRCKF is better than FSRCKF, FCKF and SRCKF in estimating the SOC of lithium batteries under the Federal Urban Driving Schedule (FUDS), Beijing Dynamic Stress Test (BJDST) and US06 Highway Driving Schedule tests, especially when R0 is inaccurate

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Abstract Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness