Physics-based model predictive control for power capability estimation of lithium-ion batteries

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today\u27s high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate

Research and Modeling of the Bidirectional Half-Bridge Current-Doubler DC/DC Converter

Due to its high step-up voltage ratio, high utilization rate, and good stability, the bidirectional half-bridge current-doubler topology is widely used in lithium battery system. This paper will further analyze the bidirectional half-bridge current-doubler topology. Taking into account the fact that the current is not equal to the two times current inductance may lead to a greater transformer magnetizing current leaving the transformer core saturation occurring. This paper will focus on the circuit modeling of steady-state analysis and small signal analysis, analyzing the influence parameters for the inductor current by steady-state model and analyzing the stability of the system by the small signal model. The PID controllers and soft start algorithm are designed. Then the influence of circuit parameters on the steady state and the effect of soft start algorithm is verified, and finally the function of the soft start algorithm is achieved by the experimental prototype

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

Considered as one of the ultimate energy storage technologies for electrified transportation, the emerging all-solid-state batteries (ASSBs) have attracted immense attention due to their superior thermal stability, increased power and energy densities, and prolonged cycle life. To achieve the expected high performance, practical applications of ASSBs require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the ASSB safety, health, and cycling performance can be optimized under a wide range of operating conditions. A control-oriented modeling framework is thus established in this work by systematically simplifying a rigorous partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Specifically, partial fraction expansion and moment matching are used to obtain ordinary differential equation based reduced-order models (ROMs). By expressing the models in a canonical circuit form, excellent properties for control design such as structural simplicity and full observability are revealed. Compared to the original PDE model, the developed ROMs have demonstrated high fidelity at significantly improved computational efficiency. Extensive comparisons have also been conducted to verify its superiority to the prevailing models due to the consideration of concentration-dependent diffusion and migration. Such ROMs can thus be used for advanced control design in future intelligent management systems of ASSBs

Multi‐Scale Microstructural Thermoelectric Materials: Transport Behavior, Non‐Equilibrium Preparation, and Applications

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137226/1/adma201602013_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137226/2/adma201602013.pd

Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China

Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation

Gut-joint axis in knee synovitis: gut fungal dysbiosis and altered fungi–bacteria correlation network identified in a community-based study

Objectives: Knee synovitis is a highly prevalent and potentially curable condition for knee pain; however, its pathogenesis remains unclear. We sought to assess the associations of the gut fungal microbiota and the fungi–bacteria correlation network with knee synovitis. Methods: Participants were derived from a community-based cross-sectional study. We performed an ultrasound examination of both knees. A knee was defined as having synovitis if its synovium was ≥4 mm and/or Power Doppler (PD) signal was within the knee synovium area (PD synovitis). We collected faecal specimens from each participant and assessed gut fungal and bacterial microbiota using internal transcribed spacer 2 and shotgun metagenomic sequencing. We examined the relation of α-diversity, β-diversity, the relative abundance of taxa and the interkingdom correlations to knee synovitis. Results: Among 977 participants (mean age: 63.2 years; women: 58.8%), 191 (19.5%) had knee synovitis. β-diversity of the gut fungal microbiota, but not α-diversity, was significantly associated with prevalent knee synovitis. The fungal genus Schizophyllum was inversely correlated with the prevalence and activity (ie, control, synovitis without PD signal and PD synovitis) of knee synovitis. Compared with those without synovitis, the fungi–bacteria correlation network in patients with knee synovitis was smaller (nodes: 93 vs 153; edges: 107 vs 244), and the average number of neighbours was fewer (2.3 vs 3.2). Conclusion: Alterations of gut fungal microbiota and the fungi–bacteria correlation network are associated with knee synovitis. These novel findings may help understand the mechanisms of the gut-joint axis in knee synovitis and suggest potential targets for future treatment