A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena

State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework

State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

Multiphysics modeling approach for micro electro-thermo-mechanical actuator: failure mechanisms coupled analysis

The lifetime of micro electro-thermo-mechanical actuators with complex electro-thermo-mechanical coupling mechanisms can be decreased significantly due to unexpected failure events. Even more serious is the fact that various failures are tightly coupled due to micro-size and multi-physics effects. Interrelation between performance and potential failures should be established to predict reliability of actuators and improve their design. Thus, a multiphysics modeling approach is proposed to evaluate such interactive effects of failure mechanisms on actuators, where potential failures are pre-analyzed via FMMEA (Failure Modes, Mechanisms, and Effects Analysis) tool for guiding the electro-thermo-mechanical-reliability modeling process. Peak values of temperature, thermal stresses/strains and tip deflection are estimated as indicators for various failure modes and factors (e.g. residual stresses, thermal fatigue, electrical overstress, plastic deformation and parameter variations). Compared with analytical solutions and experimental data, the obtained simulation results were found suitable for coupled performance and reliability analysis of micro actuators and assessment of their design

Effects of the manufacturing process on the reliability of the multilayer structure in MetalMUMPs actuators: Residual stresses and variation of design parameters

© 2017 by the authors. Potential problems induced by the multilayered manufacturing process pose a serious threat to the long-term reliability of MEMSCAP® actuators under in-service thermal cycling. Damage would initiate and propagate in different material layers because of a large mismatch of their thermal expansions. In this research, residual stresses and variations of design parameters induced by metal multi-user micro electromechanical system processes (MetalMUMPs) were examined to evaluate their effects on the thermal fatigue lifetime of the multilayer structure and, thus, to improve MEMSCAP® design. Since testing in such micro internal structure is difficult to conduct and traditional testing schemes are destructive, a numerical subdomain method based on a finite element technique was employed. Thermomechanical deformation from metal to insulator layers under in-service temperature cycling (obtained from the multiphysics model of the entire actuator, which was validated by experimental and specified analytical solutions) was accurately estimated to define failures with a significant efficiency and feasibility. Simulation results showed that critical failure modes included interface delamination, plastic deformation, micro cracking, and thermal fatigue, similarly to what was concluded in the MEMSCAP® technical report

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology

Reliability Analysis of Load-Sharing K-out-of-N System Considering Component Degradation

The K-out-of-N configuration is a typical form of redundancy techniques to improve system reliability, where at least K-out-of-N components must work for successful operation of system. When the components are degraded, more components are needed to meet the system requirement, which means that the value of K has to increase. The current reliability analysis methods overestimate the reliability, because using constant K ignores the degradation effect. In a load-sharing system with degrading components, the workload shared on each surviving component will increase after a random component failure, resulting in higher failure rate and increased performance degradation rate. This paper proposes a method combining a tampered failure rate model with a performance degradation model to analyze the reliability of load-sharing K-out-of-N system with degrading components. The proposed method considers the value of K as a variable which is derived by the performance degradation model. Also, the load-sharing effect is evaluated by the tampered failure rate model. Monte-Carlo simulation procedure is used to estimate the discrete probability distribution of K. The case of a solar panel is studied in this paper, and the result shows that the reliability considering component degradation is less than that ignoring component degradation

A Numerical Study on Influence of Temperature on Lubricant Film Characteristics of the Piston/Cylinder Interface in Axial Piston Pumps

The loss of kinetic energy of moving parts due to viscous friction of lubricant causes the reduction of piston pump efficiency. The viscosity of lubricant film is mainly affected by the thermal effect. In order to improve energy efficiency of piston pump, this research presents a numerical method to analyze the lubricant film characteristics in axial piston pumps, considering the thermal effect by the coupled multi-disciplinary model, which includes the fluid flow field expressed by Reynolds equation, temperature field expressed by energy equation and heat transfer equation, kinematics expressed by the motion equation. The velocity and temperature distributions of the gap flow of piston/cylinder interface in steady state are firstly numerically computed. Then the distributions are validated by the experiment. Finally, by changing the thermal boundary condition, the influence of thermal effect on the lubricant film, the eccentricity and the contact time between the piston and cylinder are analyzed. Results show that with the increase of temperature, the contact time increases in the form of a hyperbolic tangent function, which will reduce the efficiency of the axial piston pump. There is a critical temperature beyond which the contact time will increase rapidly, thus this temperature is the considered as a key point for the temperature design

Model niezawodności dla systemów typu k-z-n z podziałem obciążenia podlegających uszkodzeniom parametrycznym i katastroficznym, w których zachodzi zależność między obciążeniem pracą a skutkami obciążeń losowych

A component in a k-out-of-n system may experience soft and hard failures resulting from exposure to natural degradation and random shocks. Due to load-sharing characteristics, once a component fails, the surviving components share an increased workload, which increases their own degradation rates. Moreover, under the larger workload, random shocks may cause larger abrupt degradation increments and larger shock sizes. Therefore, the system experiences the dependent workload and shock effects (DWSEs). Such dependence will cause the load-sharing system to fail more easily, though it is often not considered in existing methods. In this paper, to evaluate the system reliability more accurately, we develop a novel reliability model for load-sharing k-out-of-n systems with DWSEs. In the model, the joint probability density function of shock effects to soft and hard failures is developed to describe the DWSEs on a component. To derive an analytical expression of system reliability with load-sharing characteristics and DWSEs, conditional probability density function is used to model the random component failure times. A load-sharing MicroElectro-Mechanical System (MEMS) is then utilized to illustrate the effectiveness of the reliability model.Element systemu k-z-n może ulegać uszkodzeniom parametrycznym i katastroficznym wynikającym z ekspozycji na naturalne procesy degradacji i obciążenia losowe. Ze względu na równomierny podział obciążenia między wszystkie elementy systemu, gdy jeden element ulega awarii, obciążenie pracą przypadające na pozostałe komponenty zwiększa się, podnosząc tempo degradacji każdego z nich. Ponadto, przy większym obciążeniu pracą, obciążenia losowe mogą powodować większe nagłe przyrosty degradacji i zwiększać rozmiary obciążeń. Mówi się wtedy o istnieniu zależności między obciążeniem pracą a skutkami obciążeń losowych (dependent workload and schock effects (DWSE). Taka zależność powoduje, że system z podziałem obciążeń łatwiej ulega uszkodzeniom. Fakt ten jest często pomijany w obecnie stosowanych metodach oceny niezawodności. W niniejszym artykule przedstawiamy nowatorski model oceny niezawodności systemów k-z-n z podziałem obciążenia i zależnością DWSE, który pozwala dokładniej ocenić niezawodność takich systemów. W modelu, opracowano wspólną funkcję gęstości prawdopodobieństwa skutków obciążeń losowych dla uszkodzeń parametrycznych i katastroficznych, która pozwala opisać zależność DWSE dla elementu systemu. Aby wyprowadzić analityczne wyrażenie niezawodności systemu z podziałem obciążenia i DWSE, do modelowania czasów losowych uszkodzeń elementów systemu wykorzystano funkcję warunkowej gęstości prawdopodobieństwa. Skuteczność modelu niezawodności zilustrowano na przykładzie układu mikroelektromechanicznego z podziałem obciążenia (MEMS)