Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems. Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life. In this study, the key lithium-ion battery SOC estimation technologies are summarized. First, the research status of lithium-ion battery modeling is introduced. Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed. Third, the development status and advantages and disadvantages of SOC estimation methods are summarized. Finally, the current research problems and prospects for development trends are summarized.     

Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification

In order to mitigate the degradation and prolong the lifetime of polymer electrolyte membrane fuel cells, advanced, model-based control strategies are becoming indispensable. Thereby, the availability of accurate yet computationally efficient fuel cell models is of crucial importance. Associated with this is the need to efficiently parameterize a given model to a concise and cost-effective experimental data set. A challenging task due to the large number of unknown parameters and the resulting complex optimization problem. In this work, a parameterization scheme based on the simultaneous estimation of multiple structured state space models, obtained by analytic linearization of a candidate fuel cell stack model, is proposed. These local linear models have the advantage of high computational efficiency, regaining the desired flexibility required for the typically iterative task of model parameterization. Due to the analytic derivation of the local linear models, the relation to the original parameters of the non-linear model is retained. Furthermore, the local linear models enable a straight-forward parameter significance and identifiability analysis with respect to experimental data. The proposed method is demonstrated using experimental data from a 30 kW commercial polymer electrolyte membrane fuel cell stack.     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Electric vehicle energy consumption modelling and estimation—A case study

Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.     

F-OFDM仿真系统设计与时频同步算法

针对新一代5G波形的F-OFDM技术开展了研究,通过把一个宽带分为若干个子带,对每个子带进行滤波处理以实现更好的通信效能。基于Simulink平台进行了F-OFDM仿真系统的搭建,重点对F-OFDM符号同步方法和频偏估计方法进行了研究与仿真分析。实验结果表明,基于Chu序列相比采用PN序列可以获得更好的同步效果,基于导频的Classen频偏估计算法相比基于CP的CFO估计算法和基于训练序列的Moose估计方法可以获得更好的频偏估计效果。     

自适应SA-PSO优化的威布尔混合分布参数估计方法及应用

针对采用传统参数估计方法得到的模型拟合误差较大的问题，建立多重威布尔混合分布参数估计的非线性最小二乘模型，并提出基于模拟退火（SA）思想的自适应粒子群（PSO）算法进行求解。在PSO算法优化过程中，采用自适应方法调整惯性权重和加速因子，加快其收敛速度；引入模拟退火机制，根据Metropolis准则确定最优粒子的取舍，改善其全局搜索能力。将该方法应用到某型柴油机喷油器失效分布的参数估计中，并与图解法、基于Levenberg-Marquardt的非线性最小二乘法、标准PSO算法、自适应PSO算法求解的结果进行比较，分析所提方法的优化性能及精度。结果表明，该方法能够有效提高多重威布尔混合分布模型参数估计的精度和效率。     

Model for practical prediction of natural carbonation in reinforced concrete: Part 1-formulation

A model is proposed for prediction of natural carbonation in reinforced concrete (RC) structures, and is potentially applicable to existing and new RC structures. The major components of the model comprise mathematical functions applied to predict the influence of concrete composition, and environmental factors on natural carbonation.This paper introduces the model concept and explains its structure including derivation, optimization and calibration. Over 163 data sets taken from a 10-year carbonation study were used in the model development and calibration. Only the experimental data that were based on outdoor natural exposure environment were employed in this research. Also in this study, the proposed model is compared with fib-Model Code 2010 using carbonation predictions generated from 346 data sets involving real world, highway structures. It is shown that the proposed model is comparably accurate and involves mainly basic tests with no major anticipated costs.     

基于广义二次互相关的低信噪比信号时延估计

针对时延估计精度受噪声影响,导致时延估计不准确的问题,在现有广义二次相关算法的基础上,提出了一种改进的广义二次相关算法,通过将二次相关函数先做指数运算,降低噪声干扰,再将傅里叶逆变换得到的相关函数做高次方运算,达到锐化峰值提高时延正确率的目的。仿真结果表明,信噪比(SNR)在0~10 dB时,改进算法的均方根误差明显优于广义二次相关算法,正确率相比于广义二次相关算法也显著提高,且在更低SNR的情况下仍然具有一定优势。