A Globally Consistent Framework for Reliability-based Trade Statistics Reconciliation in the Presence of an Entrepôt

This paper develops a mathematicla programming model to reconcile trade statistics subject to a set of global consistency conditions in the presence of an entrepot. Initial data reliability serves a key function for governing the magnitude of adjustment. Through a two-stage optimization procedure, the adjusted trade statistics are achived as solutions to a system of simultaneous equations that minimize a quadratic penalty function. As an empirical illustration, the model is applied to reconcile the 2004 trade statistics reported by China, Hong Kong, and their major trading partners, initialized with detailed estimates of bilateral trade flows, re-export markups, cif/fob ratios and data reliability indexes.trade statistics reconciliation, entrepot trade, data reliability, global consistency

Mountain pastures of Qilian Shan: plant communities, grazing impact and degradation status (Gansu province, NW China)

Environmental degradation of pasture areas in the Qilian Mountains (Gansu province, NW China) has increased in recent years. Soil erosion and loss of biodiversity caused by overgrazing is widespread. Changes in plant cover, however, have not been analysed so far. The aim of this paper is to identify plant communities and to detect grazing-induced changes in vegetation patterns. Quantitative and qualitative releve data were collected for community classification and to analyse gradual changes in vegetation patterns along altitudinal and grazing gradients. Detrended correspondence analysis (DCA) was used to analyse variation in relationships between vegetation, environmental factors and differential grazing pressure. The results of the DCA showed apparent variation in plant communities along the grazing gradient. Two factors – altitude and exposure – had the strongest impact on plant community distribution. Comparing monitoring data for the most recent nine years, a trend of pasture deterioration, plant community successions and shift in dominant species becomes obvious. In order to increase grassland quality, sustainable pasture management strategies should be implemented

An improved genetic-backpropagation neural network for state of charge estimation of lithium-ion batteries.

The state of charge estimation with high precision plays an important role in the usage of lithium-ion batteries in electronic vehicles. An improved genetic-backpropagation neural network (GA-BPNN) is proposed to predict the state of charge with high precision under complex working conditions. Specifically, the elite retention strategy is introduced to genetic operations to enhance the efficiency of the algorithm. Moreover, a further performance comparison of the improved GA-BPNN is achieved to prove its effectiveness. The experimental results show that the accuracy of the improved GA-BPNN is 7.92% and 6.71% under BBDST and DST working conditions, which are higher than that of traditional methods

A novel adaptive back propagation neural network-unscented Kalman filtering algorithm for accurate lithium-ion battery state of charge estimation.

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process

The power state estimation method for high energy ternary lithium-ion batteries based on the online collaborative equivalent modeling and adaptive correction-unscented Kalman filter.

Accurate power state estimation plays an important role in the real-time working state monitoring and safety control of high energy lithium-ion batteries. To solve the difficulty and low accuracy problems in its real-time power state estimation under various operating conditions, the working characteristics of the lithium cobalt oxide batteries are analyzed comprehensively under various operating conditions. An improved collaborative equivalent model is established to characterize its working characteristics and then the initial power state value is calibrated by using the experimental relationship between open circuit voltage and state of charge considering the importance of the precious estimation accuracy for the later iterate calculation and correction. And then, an adaptive correction - Unscented Kalman Filter algorithm is put forward and applied for the state of charge estimation and output voltage tracking so as to realize the real-time high-precision lithium-ion battery power state estimation. The experimental results show that the established model can predict the power state of high energy lithium-ion batteries conveniently with high convergency speed within 30 seconds, accurate output voltage tracking effect within 32 mV and high accuracy, the max estimation error of which is 3.87%, providing an effective working state monitoring and safety protection method in the cleaner production and power supply processes of the high energy lithium-ion batteries

A novel gaussian particle swarms optimized particle filter algorithm for the state of charge estimation of lithium-ion batteries.

A gaussian particle swarm optimized particle filter estimation method, along with the second-order resistance-capacitance model, is proposed for the state of charge estimation of lithium-ion battery in electric vehicles. Based on the particle filter method, it exploits the strong optimality-seeking ability of the particle swarm algorithm, suppressing algorithm degradation and particle impoverishment by improving the importance distribution. This method also introduces normally distributed decay inertia weights to enhance the global search capability of the particle swarm optimization algorithm, which improves the convergence of this estimation method. As can be known from the experimental results that the proposed method has stronger robustness and higher filter efficiency with the estimation error steadily maintained within 0.89% in the constant current discharge experiment. This method is insensitive to the initial amount and distribution of particles, achieving adaptive and stable tracking in the state of charge for lithium-ion batteries

A novel fractional-order extended Kalman filtering method for on-line joint state estimation and parameter identification of the high power li-ion batteries.

To ensure the reliability and sustainability of the energy storage system, it is important to accurately estimate the state of charge of the battery management system. The Li-ion battery is established based on fractional-order model, and the model parameters are identified online using particle swarm optimization combined with the forgetting factor recursive least square method. On this basis, a novel fractional-order extended Kalman filter method for on-line joint state estimation and parameter identification is proposed. This method can update the parameter model of Li-ion battery in real-time, which not only improves the accuracy of the battery model but also improves the accuracy of SOC estimation. Finally, to verify the accuracy and superiority of the method, the integral order extended Kalman filter, fractional-order extended Kalman filter are compared with the proposed method under the BBDST test schedule. Experimental results show that the algorithm has the highest SOC estimation accuracy and the smallest estimation error (1.5 %.). The results indicate that the fractional-order model can better describe the dynamic characteristics of Li-ion battery, and the adaptive scheme can significantly suppress noise measurement errors and battery model errors. The algorithm realizes online parameter identification and can be used in engineering applications

An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs.

A novel online adaptive state of charge (SOC) estimation method is proposed, aiming to characterize the capacity state of all the connected cells in lithium-ion battery (LIB) packs. This method is realized using the extended Kalman filter (EKF) combined with Ampere-hour (Ah) integration and open circuit voltage (OCV) methods, in which the time-scale implementation is designed to reduce the computational cost and accommodate uncertain or time-varying parameters. The working principle of power LIBs and their basic characteristics are analysed by using the combined equivalent circuit model (ECM), which takes the discharging current rates and temperature as the core impacts, to realize the estimation. The original estimation value is initialized by using the Ah integral method, and then corrected by measuring the cell voltage to obtain the optimal estimation effect. Experiments under dynamic current conditions are performed to verify the accuracy and the real-time performance of this proposed method, the analysed result of which indicates that its good performance is in line with the estimation accuracy and real-time requirement of high-power LIB packs. The proposed multimodel SOC estimation method may be used in the real-time monitoring of the high-power LIB pack dynamic applications for working state measurement and control

A novel joint support vector machine-cubature Kalman filtering method for adaptive state of charge prediction of lithium-ion batteries.

Accurate estimation of SOC of lithium-ion batteries has always been an important work in the battery management system. However, it is often very difficult to accurately estimate the SOC of lithium-ion batteries. Therefore, a novel joint support vector machine - cubature Kalman filtering (SVM-CKF) method is proposed in this paper. SVM is used to train the output data of the CKF algorithm to obtain the model. Meanwhile, the output data of the model is used to compensate the original SOC, to obtain a more accurate estimate of SOC. After the SVM-CKF algorithm is introduced, the amount of data needed for prediction is reduced. By using Beijing Bus Dynamic Stress Test (BBDST) and the Dynamic Stress Test (DST) condition to verify the training model, the results show that the SVM-CKF algorithm can significantly improve the estimation accuracy of Lithium-ion battery SOC, and the maximum error of SOC prediction for BBDST condition is 0.800%, which is reduced by 0.500% compared with CKF algorithm. The maximum error of SOC prediction under DST condition is about 0.450%, which is 1.350% less than that of the CKF algorithm. The overall algorithm has a great improvement in generalization ability, which lays a foundation for subsequent research on SOC prediction

A novel fireworks factor and improved elite strategy based on back propagation neural networks for state-of-charge estimation of lithium-ion batteries.

The state of charge (SOC) of Lithium-ion battery is one of the key parameters of the battery management system. In the SOC estimation algorithm, the Back Propagation (BP) neural network algorithm is easy to converge to the local optimal solution, which leads to the problem of low accuracy based on the BP network. It is proposed that the Fireworks Elite Genetic Algorithm (FEG-BP) is used to optimize the BP neural network, which can not only solve the problem of the traditional neural network algorithm that is easy to fall into the local maximum optimal solution but also solve the limitation of the traditional neural network algorithm. The searchability of the improved algorithm has been significantly enhanced, and the error has become smaller and the propagation speed is faster. Combining the experimental data of charging and discharging, the proposed FEG-BP neural network is compared with the traditional genetic neural network algorithm (GA-BP), and the results are analyzed. The results show that the standard BP neural network genetic algorithm predicts error within 7%, while FEG-BP reduces the error to within 3%