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"state of health"
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Silent running : our family's journey to the finish line with autism
\"Running is a way of life for the Schneider family, but not in the same way as it is for most runners. Twin brothers Alex and Jamie Schneider are severely autistic--they are nonverbal and when anxiety takes over they bite themselves or slam their feet into the hard floor, they cannot tell their mother Robyn if they are hurt or hungry, and they can never be left alone. Yet they have run almost 150 races, including six marathons. The boys' parents have also turned to running to battle health issues: father Allan successfully manages his symptoms of multiple sclerosis with vitamins and miles and miles of jogging on the trails near their Long Island home, while mother Robyn, who, while undergoing chemotherapy for breast cancer six years ago, laced up her own running shoes for the first time and headed out the door, determined to run her way to recovery. In Silent Running, Robyn Schneider shares her family's incredible story of triumph in the face of enormous hurdles, and of the shared passion that has fueled their fight. It is a story of hope and of never giving up\"-- Provided by publisher.
Book
Comprehensive Real‐Time Insights for State of Health Prediction: A Comprehensive Framework for Online State of Health Assessment in Commercial Lithium‐Ion Batteries
Lithium‐ion batteries (LIBs) are widely used for energy storage in various industries due to their high energy density and long lifespan. However, degradation mechanisms may lead to hazardous conditions, such as thermal runaway. Solely relying on capacity changes for state of health (SOH) assessment is insufficient, given the complexity of LIBs. This work introduces comprehensive real‐time insights for state of health prediction (CRISP), a novel framework for comprehensive SOH assessment and degradation mechanisms identification. Using data from commercial LIBs, CRISP runs on a low‐cost Raspberry Pi with remote monitoring capabilities, generating aged anode half‐cell voltages for each reference performance test (RPT) as references for lithium plating assessment. CRISP processed the data of each RPT in ≈2.9 s and outputs multiple physical quantities for SOH evaluation. Results show that LIBs cycled at 100% depth of discharge (DOD) exhibited greater cathode material loss compared to those cycled at narrower DODs. However, no dendritic lithium deposition is detected by evaluating and correlating the physical parameters provided by CRISP. In summary, this study highlights the importance of multi‐parameter SOH assessment, demonstrating that single‐parameter methods (e.g., capacity‐based) fail to capture the full scope of LIBs health. CRISP is a low‐cost framework for state of health (SOH) assessment and degradation mechanism identification in lithium‐ion batteries. Running on a Raspberry Pi computer, it processes each reference performance test in ≈2.9 s, outputting physical quantities for SOH evaluation. Additionally, CRISP generates aged anode half‐cell voltages curves to be used as a reference for lithium plating assessment.
Journal Article
Madness : race and insanity in a Jim Crow asylum
On a cold day in March of 1911, officials marched twelve Black men into a forest in Maryland. Under the supervision of a doctor, the men were forced to clear the land, pour cement, lay bricks and harvest tobacco. When construction finished, they became the first patients of the state's Hospital for the Negro Insane. 'Madness' transports readers behind the brick walls of a Jim Crow asylum as author Antonia Hylton tells the 93-year-old history of Crownsville Hospital, one of the last segregated asylums with surviving records and a campus that still stands to this day in Maryland.
Book
An Unscented Kalman Filter-Based Robust State of Health Prediction Technique for Lithium Ion Batteries
Electric vehicles (EVs) have emerged as a promising solution for sustainable transportation. The high energy density, long cycle life, and low self-discharge rate of lithium-ion batteries make them an ideal choice for EVs. Recently, these batteries have been prone to faster decay in life span, leading to sudden failure of the battery. To avoid uncertainty among EV users with sudden battery failures, a robust health monitoring and prediction scheme is required for the EV battery management system. In this regard, the Unscented Kalman Filter (UKF)-based technique has been developed for accurate and reliable prediction of battery health status. The UKF approximates nonlinearity using a set of sigma points and propagates them via the nonlinear function to enhance battery health estimation accuracy. Furthermore, the UKF-based health estimation scheme considers the state of charge (SOC) and internal resistance of the battery. Here, the UKF-based health prediction technique is compared with the Extended Kalman filter (EKF) scheme. The robustness of the UKF and EKF-based health prognostic techniques were studied under varying initial SOC values. Under these abrupt changing conditions, the proposed UKF technique performed effectively in terms of state of health (SOH) prediction. Accurate SOH determination can help EV users to decide when the battery needs to be replaced or if adjustments need to be made to extend its life. Ultimately, accurate and reliable battery health estimation is essential in vehicular applications and plays a pivotal role in ensuring lithium-ion battery sustainability and minimizing environmental impacts.
Journal Article
Vanished in Hiawatha : the story of the Canton Asylum for Insane Indians
\"Begun as a pork-barrel project by the federal government in the early 1900s, the Canton Asylum for Insane Indians quickly became a dumping ground for inconvenient Indians. The federal institution in Canton, South Dakota, deprived many Native patients of their freedom without genuine cause, often requiring only the signature of a reservation agent. Only nine Native patients in the asylum's history were committed by court order. Without interpreters, mental evaluations, or therapeutic programs, few patients recovered. But who cared about Indians and what went on in South Dakota? After three decades of complacency, both the superintendent and the city of Canton were surprised to discover that someone did care, and that a bitter fight to shut the asylum down was about to begin. In this disturbing tale, Carla Joinson unravels the question of why this institution persisted for so many years. She also investigates the people who allowed Canton Asylum's mismanagement to reach such staggering proportions and asks why its administrators and staff were so indifferent to the misery experienced by patients. Grim Shadows is the harrowing tale of the mistreatment of Native American patients at a notorious insane asylum whose history helps us to understand the broader mistreatment of Native peoples under forced federal assimilation in the nineteenth and early twentieth centuries\"-- Provided by publisher.
Book
State of Health Trajectory Prediction Based on Multi-Output Gaussian Process Regression for Lithium-Ion Battery
Lithium-ion battery state of health (SOH) accurate prediction is of great significance to ensure the safe reliable operation of electric vehicles and energy storage systems. However, safety issues arising from the inaccurate estimation and prediction of battery SOH have caused widespread concern in academic and industrial communities. In this paper, a method is proposed to build an accurate SOH prediction model for battery packs based on multi-output Gaussian process regression (MOGPR) by employing the initial cycle data of the battery pack and the entire life cycling data of battery cells. Firstly, a battery aging experimental platform is constructed to collect battery aging data, and health indicators (HIs) that characterize battery aging are extracted. Then, the correlation between the HIs and the battery capacity is evaluated by the Pearson correlation analysis method, and the HIs that own a strong correlation to the battery capacity are screened. Finally, two MOGPR models are constructed to predict the HIs and SOH of the battery pack. Based on the first MOGPR model and the early HIs of the battery pack, the future cycle HIs can be predicted. In addition, the predicted HIs and the second MOGPR model are used to predict the SOH of the battery pack. The experimental results verify that the approach has a competitive performance; the mean and maximum values of the mean absolute error (MAE) and root mean square error (RMSE) are 1.07% and 1.42%, and 1.77% and 2.45%, respectively.
Journal Article
Becoming FDR : the personal crisis that made a president
\"In popular memory, Franklin Delano Roosevelt was the quintessential political \"natural.\" Born in 1882 to a wealthy, influential family and blessed with charisma, he seemed destined for high office from birth. Yet for all his gifts, the young Roosevelt nonetheless lacked depth, empathy, and strategic ability. Those qualities, so essential to his success as president, were skills he acquired during his eight-year struggle through illness and recovery. Becoming FDR traces the riveting story of the crucible that forged Roosevelt's political ascent. Soon after contracting polio in 1921, the former vice-presidential candidate was left paralyzed from the waist down at the age of thirty-nine. He spent nearly a decade trying to heal and rehabilitate his body and adapt to the stark new reality of his life. By the time he reemerged on the national stage, his character and his abilities had been transformed. He had become shrewd by necessity, tailoring his speeches to a new medium-radio-that allowed him to reach listeners far beyond his physical presence. Suffering had also taught Roosevelt compassion, cementing his bond with those he once famously called \"the forgotten man.\" Most crucially, he had discovered how to find hope in a seemingly hopeless situation-a genius for inspiration he employed to motivate Americans through the Great Depression and World War II. The polio years were transformative too for Eleanor Roosevelt, whose at-first reluctant appearances as her husband's surrogate sparked a drive to become a force in her own right. Tracing the physical, political, and personal transformation of the iconic president, Becoming FDR is the story of a man who found his strong, true self in the depths of a crushing challenge-and re-emerged with wisdom he would use to inspire the world\"-- Provided by publisher.
BOOK
A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery
Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.
Journal Article
The world belonged to us
\"A group of kids celebrate the joy and freedom of summer not so long ago on their Brooklyn block\"-- Provided by publisher
Book
State‐of‐health estimation for lithium‐ion batteries based on partial charging segment and stacking model fusion
State‐of‐health (SOH) estimation is essential for evaluating the aging process of lithium‐ion batteries, which can effectively guarantee the steady application of the battery system. Most existing prediction approaches apply a single model or a single feature to achieve SOH estimation based on the entire charging curve. In this paper, a multifeature‐based stacked ensemble learning framework is proposed for SOH prediction using partial charging curves. Firstly, combined with the range of state‐of‐charge (SOC) commonly used in the actual operation of vehicles, the charging segment is effectively intercepted through the mapping correlation between the SOC and the terminal voltage. Then, five relevant features characterizing the battery health status are extracted from multiple data, such as temperature, voltage, and incremental capacity profiles. Finally, a two‐level stacking ensemble framework is developed to fuse several individual estimation methods for higher SOH accuracy. To validate the performance of the proposed method, the Oxford University data set and the NASA data set are deployed for comparison experiments, and the results reveal the superior precision and robustness of the developed model in SOH estimation. In this paper, a multifeature‐based stacking ensemble learning framework is proposed for state‐of‐health (SOH) prognostic using partial charging curves. To verify the performance of the proposed method, the Oxford University data set is deployed for the comparative experiments, and the results demonstrate that the developed model has superior accuracy and robustness in SOH estimation.
Journal Article