Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems

Lithium-ion battery (LIB) sources have played an essential role in self-sustained transportation energy systems and have been widely deployed in the last few years. To realize reliable battery maintenance, identifying its electrochemical parameters is necessary. However, the battery model contains many parameters while the measurable states are only the current and voltage, inducing the identification inherently an ill-conditioned problem. A parameter identification approach is proposed, including the experiment, model, and algorithm. Electrochemical parameters are first grouped manually based on the physical properties and assigned to two sequenced tests for identification. The two tests named the quasi-static test and the dynamic test, are compressed on time for practical implementation. Proper optimization models and a sensitivity-oriented stepwise (SSO) optimization algorithm are developed to search for the optimal parameters efficiently. Typically, the Sobol method is applied to conduct the sensitivity analysis. Based on the sensitivity indexes, the SSO algorithm can decouple the mixed impacts of different parameters during the identification. For validation, numerical experiments on a typical NCM811 battery at different life stages are conducted. The proposed approach saves about half the time finding the proper parameter value. The identification accuracy of crucial parameters related to battery degradation can exceed 95\%. Case study results indicate that the identified parameters can not only improve the accuracy of the battery model but also be used as the indicator of the battery SOH

Responses of human adipose-derived mesenchymal stem cells to chemical microenvironment of the intervertebral disc

<p>Abstract</p> <p>Background</p> <p>Human adipose-derived mesenchymal stem cells (ADMSCs) may be ideal source of cells for intervertebral disc (IVD) regeneration, but the harsh chemical microenvironment of IVD may significantly influence the biological and metabolic vitality of ADMSCs and impair their repair potential. This study aimed to investigate the viability, proliferation and the expression of main matrix proteins of ADMSCs in the chemical microenvironment of IVD under normal and degeneration conditions.</p> <p>Methods</p> <p>ADMSCs were harvested from young (aged 8-12 years, n = 6) and mature (aged 33-42 years, n = 6) male donors and cultured under standard condition and IVD-like conditions (low glucose, acidity, high osmolarity, and combined conditions) for 2 weeks. Cell viability was measured by annexin V-FITC and PI staining and cell proliferation was measured by MTT assay. The expression of aggrecan and collagen-I was detected by real-time quantitative polymerase chain reaction and Western blot analysis.</p> <p>Results</p> <p>IVD-like glucose condition slightly inhibited cell viability, but increased the expression of aggrecan. In contrast, IVD-like osmolarity, acidity and the combined conditions inhibited cell viability and proliferation and the expression of aggrecan and collagen-I. ADMSCs from young and mature donors exhibited similar responses to the chemical microenvironments of IVD.</p> <p>Conclusion</p> <p>IVD-like low glucose is a positive factor but IVD-like high osmolarity and low pH are deleterious factors that affect the survival and biological behaviors of ADMSCs. These findings may promote the translational research of ADMSCs in IVD regeneration for the treatment of low back pain.</p

Label-Free Liver Tumor Segmentation

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.Comment: CVPR 202

Spatiotemporal Arbitrage of Large-Scale Portable Energy Storage for Grid Congestion Relief

Energy storage has great potential in grid congestion relief. By making large-scale energy storage portable through trucking, its capability to address grid congestion can be greatly enhanced. This paper explores a business model of large-scale portable energy storage for spatiotemporal arbitrage over nodes with congestion. We propose a spatiotemporal arbitrage model to determine the optimal operation and transportation schedules of portable storage. To validate the business model, we simulate the schedules of a Tesla Semi full of Tesla Powerpack doing arbitrage over two nodes in California with local transmission congestion. The results indicate that the contributions of portable storage to congestion relief are much greater than that of stationary storage, and that trucking storage can bring net profit in energy arbitrage applications.Comment: Submitted to IEEE PES GM 2019; 5 pages,4 figure

Joint Oscillation Damping and Inertia Provision Service for Converter-Interfaced Generation

As renewable generation becomes more prevalent, traditional power systems dominated by synchronous generators are transitioning to systems dominated by converter-interfaced generation. These devices, with their weaker damping capabilities and lower inertia, compromise the system's ability to withstand disturbances, pose a threat to system stability, and lead to oscillations and poor frequency response performance. While some new converter-interfaced generations are capable of providing superior damping and fast frequency control, there is a lack of effective measures to incentivize manufacturers to adopt them. To address this gap, this paper defines the joint oscillation damping and inertia provision services at the system level, seeking to encourage converter-interfaced generation to provide enhanced damping and fast frequency response capabilities. Our approach is anchored in a novel convex parametric formulation that combines oscillation mode and frequency stability constraints. These constraints ensure a sufficient damping ratio for all oscillation modes and maintain transient frequency trajectories within acceptable limits. They are designed to integrate smoothly into various operational and planning optimization frameworks. Using this formulation, we introduce a joint service for oscillation damping and inertia provision based on a cost-minimization problem. This facilitates the optimal allocation of damping and virtual inertia to converters, achieving both small-signal stability and frequency stability. Furthermore, we investigate the economic effects of introducing this service into a new ancillary service market, assessing its impact on system operations and cost-efficiency. Numerical tests highlight the service's efficacy in ensuring both small-signal stability and frequency stability, and offer insights into potential economic benefits.Comment: Submitted for IEEE PES journal for possible publication