Relevant bibliographies by topics / State of charge estimation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'State of charge estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "State of charge estimation"

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Bai, Wenyuan, Xinhui Zhang, Zhen Gao, Shuyu Xie, Ke Peng, and Yu Chen. "Sensorless Coestimation of Temperature and State-of-Charge for Lithium-Ion Batteries Based on a Coupled Electrothermal Model." International Journal of Energy Research 2023 (February 6, 2023): 1–18. http://dx.doi.org/10.1155/2023/4021256.

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Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled est
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Vinh, Thuy Nguyen, Chi Nguyen Van, and Vy Nguyen Van. "State-augmented adaptive sliding-mode observer for estimation of state of charge and measurement fault in lithium-ion batteries." International Journal of Applied Power Engineering (IJAPE) 14, no. 2 (2025): 291. https://doi.org/10.11591/ijape.v14.i2.pp291-299.

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Estimating the state of charge (SoC) in lithium-ion batteries (LiB) encounters challenges due to model uncertainties and sensor measurement errors. To solve this issue, this study introduces an estimator based on an innovative adaptive augmented sliding mode approach. This approach incorporates measurement faults as additional state variables to minimize the impacts of uncertainties effectively. Furthermore, based on the sliding mode framework, the design of this estimator addresses resistance to model uncertainties. However, sliding estimators commonly face the chattering issue. To counteract
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Yoo, Min Young, Jung Heon Lee, Joo-Ho Choi, Jae Sung Huh, and Woosuk Sung. "State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility." Aerospace 10, no. 6 (2023): 550. http://dx.doi.org/10.3390/aerospace10060550.

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This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate
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Chang, Wen-Yeau. "The State of Charge Estimating Methods for Battery: A Review." ISRN Applied Mathematics 2013 (July 23, 2013): 1–7. http://dx.doi.org/10.1155/2013/953792.

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An overview of new and current developments in state of charge (SOC) estimating methods for battery is given where the focus lies upon mathematical principles and practical implementations. As the battery SOC is an important parameter, which reflects the battery performance, so accurate estimation of SOC cannot only protect battery, prevent overcharge or discharge, and improve the battery life, but also let the application make rationally control strategies to achieve the purpose of saving energy. This paper gives a literature survey on the categories and mathematical methods of SOC estimation
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Li, Shuo, Song Li, Haifeng Zhao, and Yuan An. "Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system." International Journal of Distributed Sensor Networks 15, no. 12 (2019): 155014771989452. http://dx.doi.org/10.1177/1550147719894526.

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In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-pro
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Lai, Xin, Ming Yuan, Xiaopeng Tang, et al. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing." Energies 15, no. 19 (2022): 7416. http://dx.doi.org/10.3390/en15197416.

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State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time u
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Asghar, Furqan, Muhammad Talha, Sung Ho Kim, and In-Ho Ra. "Simulation Study on Battery State of Charge Estimation Using Kalman Filter." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 6 (2016): 861–66. http://dx.doi.org/10.20965/jaciii.2016.p0861.

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Low power dissipation and maximum battery run-time are crucial in portable electronics and EV’s. Battery characteristics and performance varied at different operating conditions. By using accurate, efficient circuit and battery models, designers can predict and optimize battery runtime, current state of charge (SOC) and circuit performance. A great factor in determining the stability of battery system lies within the state of charge estimation. Failing to predict SOC will cause overcharge or over discharge which potentially will bring permanent damage to the battery cells. Open circuit voltage
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Figueroa-Santos, Miriam A., Jason B. Siegel, and Anna G. Stefanopoulou. "Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations." Energies 13, no. 10 (2020): 2653. http://dx.doi.org/10.3390/en13102653.

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Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as m
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Martí-Florences, Miquel, Piñol Andreu Cecilia, Alejandro Clemente, and Ramon Costa-Castelló. "SoC Estimation in Lithium-Ion Batteries with Noisy Measurements and Absence of Excitation." Batteries 9, no. 12 (2023): 578. https://doi.org/10.3390/batteries9120578.

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Accurate State-of-Charge estimation is crucial for applications that utilise lithium-ion batteries. In real-time scenarios, battery models tend to present significant uncertainty, making it desirable to jointly estimate both the State of Charge and relevant unknown model parameters. However, parameter estimation typically necessitates that the battery input signals induce a persistence of excitation property, a need which is often not met in practical operations. This document introduces a joint state of charge/parameter estimator that relaxes this stringent requirement. This estimator is base
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El Maliki, Anas, Abdessamad Benlafkih, Kamal Anoune, and Abdelkader Hadjoudja. "Reduce state of charge estimation errors with an extended Kalman filter algorithm." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 57. http://dx.doi.org/10.11591/ijece.v14i1.pp57-65.

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Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm de
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Dissertations / Theses on the topic "State of charge estimation"

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Boman, Carl. "Embedded impedance based state-of-charge estimation." Thesis, KTH, Maskinkonstruktion (Inst.), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177673.

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Estimating the battery state-of-charge is an important aspect of prolonging battery life and optimizing discharge cycles. In this thesis, a hybrid method for estimating state-of-charge for lead-acid batteries is developed and implemented in a motor controller. The method uses a combination of impedance measurements and a linear regression model for estimation. Measurements are done at stand-still. The method does not require sensors that are external to the motor controller or auxiliary battery connections other than terminal connections. The final estimation model shows good results but exhib
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Zhang, Klaus. "Comparison of Nonlinear Filtering Methods for Battery State of Charge Estimation." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1896.

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In battery management systems, the main figure of merit is the battery's SOC, typically obtained from voltage and current measurements. Present estimation methods use simplified battery models that do not fully capture the electrical characteristics of the battery, which are useful for system design. This thesis studied SOC estimation for a lithium-ion battery using a nonlinear, electrical-circuit battery model that better describes the electrical characteristics of the battery. The extended Kalman filter, unscented Kalman filter, third-order and fifth-order cubature Kalman filter, and the sta
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Bartlett, Alexander P. "Electrochemical Model-Based State of Charge and State of Health Estimation of Lithium-Ion Batteries." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1432640190.

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Schmidt, Alexander Patrick [Verfasser]. "A Novel Electrochemical Battery Model For State Of Charge And State Of Health Estimation / Alexander Patrick Schmidt." Aachen : Shaker, 2010. http://d-nb.info/1084536315/34.

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Hyun, Ji Hoon. "State of Health Estimation System for Lead-Acid Car Batteries Through Cranking Voltage Monitoring." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71796.

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The work in this thesis is focused on the development and validation of an automotive battery monitoring system that estimates the health of a lead-acid battery during engine cranking and provides a low state of health (SOH) warning of potential battery failure. A reliable SOH estimation should assist users in preventing a sudden battery failure and planning for battery replacement in a timely manner. Most commercial battery health estimation systems use the impedance of a battery to estimate the SOH with battery voltage and current; however, using a current sensor increases the installation
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Martinsson, Patrik. "State of Charge Estimation in a High Temperature Sodium Nickel Chloride Battery Using Kalman Filter." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11173.

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<p>In today’s heavy industry there are applications demanding high power supply in certain periods of a working cycle. A typical case might be startup of heavy machinery or just keeping a certain point in a distribution network at a certain energy level. To deal with this different techniques might be used, one way is to introduce a battery as an energy reserve in the system. One battery studied at ABB for this purpose is the so called High Temperature Sodium Nickel Chloride battery and a model of this battery has been developed at ABB. When operating a battery of the mentioned type in an appli
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Shen, Jing Cong. "Time efficient state-of-charge estimation using open circuit voltage and the logarithmic modelling for battery management system." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59115.

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Battery management systems are important devices for protecting batteries in various electrical and electronic applications. One of the most important features of a properly designed battery management system is the diagnostic techniques for estimating the remaining usable charge in batteries in a time effective manner. This thesis presents two time-efficient and reliable techniques that do not require complex electronic hardware for application. One technique concerns the open-circuit voltage characteristics of a battery while the other technique considers the logarithmic modeling of the equi
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Samolyk, Mateusz, and Jakub Sobczak. "Development of an algorithm for estimating Lead-Acid Battery State of Charge and State of Health." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2937.

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In this paper, a state of charge (SOC) and a state of health (SOH) estimation method for lead-acid batteries are presented. In the algorithm the measurements of battery’s terminal voltage, current and temperature are used in the process of SOC calculation. The thesis was written in cooperation with Micropower AB. The algorithm was designed to fulfill the specific requirements of the electric vehicles application: an error below 5% of SOC, computational simplicity and the possibility of being implemented in a basic programming languages. The current used method at Micropower, Coulomb counting,
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Hu, Yiran. "Identification and State Estimation for Linear Parameter Varying Systems with Application to Battery Management System Design." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281368350.

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Narayan, Anand. "State and Parametric Estimation of Li-Ion Batteries in Electrified Vehicles." Thesis, KTH, Elkraftteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217124.

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The increasing demand for electric vehicles (EVs) has led to technological advancementsin the field of battery technology. State of charge (SOC) estimation is a vital function ofthe battery management system - the heart of EVs, and Kalman filtering is a commonmethod for SOC estimation. Due to the non uniformities in tuning and testing scenarios,quantifying performance of SOC estimation algorithms is difficult. Gathering data fordifferent operational scenarios is also cumbersome. In this thesis, SOC estimation algorithmsare developed and tested for a variety of scenarios like varying sensor noi
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Books on the topic "State of charge estimation"

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Heijden, F. van der, R. P. W. Duin, D. de Ridder, and D. M. J. Tax. Classification, Parameter Estimation and State Estimation. John Wiley & Sons, Ltd, 2004. http://dx.doi.org/10.1002/0470090154.

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de Ridder, Dick, David M. J. Tax, Bangjun Lei, et al. Classification, Parameter Estimation and State Estimation. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119152484.

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Simon, Dan. Optimal State Estimation. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470045345.

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Paris, Matteo, and Jaroslav Řeháček, eds. Quantum State Estimation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98673.

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Simon, Dan. Optimal State Estimation. John Wiley & Sons, Ltd., 2006.

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Matteo, Paris, and Řeháček Jaroslav, eds. Quantum state estimation. Springer, 2004.

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Shi, Dawei, Ling Shi, and Tongwen Chen. Event-Based State Estimation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26606-0.

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Chernousʹko, F. L. State estimation for dynamic systems. CRC Press, 1994.

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author, Dunn K.-P. (Keh-Ping), ed. Applied state estimation and association. MIT Press, 2015.

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Mohammed, Mousa-Booth, Dugurawa M. S, and Taura Ahmed, eds. Kano State, those in charge. Information Division, Ministry of Home Affairs, Information, and Culture, 1988.

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Book chapters on the topic "State of charge estimation"

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Deng, Dan, Daniel-Ioan Stroe, Jialu Qiao, et al. "Battery State of Charge Estimation." In Multidimensional Lithium-Ion Battery Status Monitoring. CRC Press, 2022. http://dx.doi.org/10.1201/9781003333791-3.

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Vepa, Ranjan. "Batteries: Modeling and State of Charge Estimation." In Lecture Notes in Energy. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5400-6_7.

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Kaushik, Kaustubh, Devang Sureka, and H. V. Gururaja Rao. "Review of Battery State-of-Charge Estimation Algorithms." In Advances in Renewable Energy and Electric Vehicles. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1642-6_29.

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Gaikwad, Kshitija A., and Vijaykumar Bhanuse. "State of Charge Estimation Using Extended Kalman Filter." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8391-9_7.

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Barsali, Stefano, Massimo Ceraolo, Jiajing Li, Giovanni Lutzemberger, and Claudio Scarpelli. "Luenberger Observer for Lithium Battery State-of-Charge Estimation." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37161-6_49.

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Kallimani, Rakhee, Shweta Gulannavar, Krishna Pai, and Prachi Patil. "A Detailed Study on State of Charge Estimation Methods." In Proceedings of Third International Conference on Communication, Computing and Electronics Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8862-1_14.

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Fotouhi, Abbas, Karsten Propp, Daniel J. Auger, and Stefano Longo. "State of Charge and State of Health Estimation Over the Battery Lifespan." In Behaviour of Lithium-Ion Batteries in Electric Vehicles. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69950-9_11.

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Elmarghichi, Mouncef, Mostafa Bouzi, Naoufal Ettalabi, and Mounir Derri. "Comparison of State of Charge Estimation Algorithms for Lithium Battery." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6259-4_30.

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Grilli, Michele, Jennifer Guaitini, Simone Orcioni, and Massimo Conti. "Impact of Parameter Estimation Accuracy on State of Charge Estimation Using Extended Kalman Filter." In Lecture Notes in Electrical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84100-2_55.

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Dewalkar, Samruddhi, and S. P. Nangrani. "Artificial Intelligence-Based State of Charge Estimation of Electric Vehicle Battery." In Springer Proceedings in Energy. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6879-1_67.

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Conference papers on the topic "State of charge estimation"

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Sabatino, Simona, Vito Calderaro, Vincenzo Galdi, and Giuseppe Greber. "State-of-Charge, State-of-Health and State-of-Power Estimation for Traction Batteries." In 2024 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). IEEE, 2024. https://doi.org/10.1109/esars-itec60450.2024.10819760.

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Li, Heng, Zitao Zhou, Ren Zhu, Hui Peng, and Jiehao Li. "State-of-Charge Estimation of Supercapacitors for Reconfigurable Circuits." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10832054.

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S, Tharunkumar, Thanuj G, Charuvarthan T, Sai Karthikeya V, Pradeesh Prem Kumar, and Rahul Satheesh. "LSTM-Driven State of Charge Estimation for Battery Management." In 2024 IEEE 11th Power India International Conference (PIICON). IEEE, 2024. https://doi.org/10.1109/piicon63519.2024.10995078.

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Gadia, Vedika, Adarsh Jaju, P. V. Subrahmanyam, and Debashisha Jena. "Koopman Theory Inspired Neural Network for State of Charge Estimation." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677261.

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Mohan, Nikhil C., Hemali Priyadarshi, Vivek Kumar Patidar, Bijaya Ketan Panigrahi, and Saurabh Saxena. "Battery State of Charge Estimation with Separable Temporal Convolutional Networks." In 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2024. http://dx.doi.org/10.1109/sefet61574.2024.10717918.

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R, Aravindharaj, Athiniraj K, and T. V. Narmadha. "Optimised State of Charge Estimation for Battery Using ML Technique." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031976.

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Castillo, Brian, and Masood Shahverdi. "Battery Parameter Extraction and Real-Time State of Charge Estimation." In 2025 7th Global Power, Energy and Communication Conference (GPECOM). IEEE, 2025. https://doi.org/10.1109/gpecom65896.2025.11061954.

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Makuwatsine, Tatenda Trevor, and Manjeet Singh. "State of Charge (SOC) and State of Health (SOH) Estimation for Electric Vehicle Battery." In 2024 International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3). IEEE, 2024. https://doi.org/10.1109/ic2e362166.2024.10827735.

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Li, Yonghua, and R. Dyche Anderson. "Switching Adaptive Observer for Lithium-Ion Battery State of Charge Estimation." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6061.

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A switching adaptive observer is proposed for estimation of state of charge (SOC) for lithium ion batteries used in electrified automotive propulsion systems. The base observer includes (i) a parameter estimation subsystem including a recursive parameter estimator for identifying battery parameters and (ii) an open circuit voltage (OCV) estimation subsystem including a nonlinear adaptive observer for estimating battery OCV. A timer as well as excitation level determination decides when the ampere-hour integration based SOC or estimated OCV based SOC is used as output. Using this approach, tran
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Shuo Pang, J. Farrell, Jie Du, and M. Barth. "Battery state-of-charge estimation." In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.945964.

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Reports on the topic "State of charge estimation"

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Yang, Yu, Hen-Geul Yeh, and Cesar Ortiz. Battery Management System Development for Electric Vehicles and Fast Charging Infrastructure Improvement. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2325.

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The electric vehicle (EV) has become increasingly popular due to its being zero-emission. However, a significant challenge faced by EV drivers is the range anxiety associated with battery usage. Addressing this concern, this project develops a more efficient battery management system (BMS) for electric vehicles based on a real-time, state-of-charge (SOC) estimation. The proposed study delivers three modules: (1) a new equivalent circuit model (ECM) for lithium-ion batteries, (2) a new SOC estimator based on the moving horizon method, and (3) an on-board FPGA implementation of the classical Cou
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Pizzolon, Fiorella, and Alejandro Rasteletti. Assessing Different Methodologies for the Estimation of Uruguay's Structural Fiscal Balance. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0009143.

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This document assesses the advantages and disadvantages of using different methodologies to estimate Uruguay's structural fiscal balance. Results indicate that disaggregated methods or methods with more than one economic cycle are the more appropriate ones, as they are better suited to handle the changes in revenues and expenditures composition observed in recent years. The document also discusses some of the difficulties faced while estimating the temporary components in interest payments and in the primary results of state-owned enterprises. As model-based corrections tend to be unreliable;
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Bajwa, Abdullah, and Timothy Jacobs. PR-457-17201-R02 Residual Gas Fraction Estimation Based on Measured Engine Parameters. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011558.

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Gas exchange processes in two-stroke internal combustion engines, commonly referred to as scavenging, are responsible for removing the exhaust gases in the combustion chamber and preparing the combustible fuel-oxidizer mixture that undergoes combustion and converts the chemical energy of the fuel into mechanical work. Scavenging is a complicated phenomenon because of the simultaneous introduction of fresh gases into the engine cylinder through the intake ports, and the expulsion of combustion products from the previous cycles through the exhaust ports. A non-negligible fraction of the gaseous
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Bajwa, Abdullah, and Timothy Jacobs. PR-457-17201-R03 Residual Gas Fraction Estimation Based on Measured In-Cylinder Pressure - Phase III. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0011996.

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An experimental study was carried out to characterize the scavenging behavior of a cross-scavenged, piston-aspirated, two-stroke, natural gas engine to aid in the development of computationally inexpensive simple scavenging models for onboard engine control by (1) studying the effects of changing operational parameters on the engine's scavenging performance, and (2) identifying the underlying phenomena driving the observed effects. Tracer based methods were used to quantify the scavenging and trapping performance of the engine - CO2 was used as a tracer for combustion products and pre-mixed fu
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Brosh, Arieh, David Robertshaw, Yoav Aharoni, Zvi Holzer, Mario Gutman, and Amichai Arieli. Estimation of Energy Expenditure of Free Living and Growing Domesticated Ruminants by Heart Rate Measurement. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7580685.bard.

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Research objectives were: 1) To study the effect of diet energy density, level of exercise, thermal conditions and reproductive state on cardiovascular function as it relates to oxygen (O2) mobilization. 2) To validate the use of heart rate (HR) to predict energy expenditure (EE) of ruminants, by measuring and calculating the energy balance components at different productive and reproductive states. 3) To validate the use of HR to identify changes in the metabolizable energy (ME) and ME intake (MEI) of grazing ruminants. Background: The development of an effective method for the measurement of
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Roa, Julio, Joseph Oldham, and Marina Lima. Recognizing the Potential to Reduce GHG Emissions Through Air Transportation Electrification. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2223.

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California is aggressively moving forward with efforts to deploy zero-emission transportation technology to fight climate change, especially the Greenhouse Gas (GHG) emissions from the high-impact transportation sector. However, to date, the investments California has made with Cap-and-Trade funding have focused on ground transportation and some marine sources and not the aircraft at the over 140 airports in the state. Through a California-focused comprehensive GHG emissions analysis, this research project seeks to determine how RAM using electric/hybrid electric aircraft can provide new high-
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Shepard, K. W., and J. W. Kim. Low-charge-state linac. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/166408.

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Viswanathan, Sivaswamy, and Allen Charkey. Aircraft Battery State of Charge and Charge Control System. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada194979.

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Viswanathan, Sivaswamy, and Allen Charkey. Aircraft Battery State of Charge and Charge Control System. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada169411.

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Shepard, K. W., and J. W. Kim. Low-charge-state RFQ injector. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/166409.

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