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Graphite lithiation and capacity fade monitoring of lithium ion batteries using optical fibers
摘要： Increasing the e?ciency and safety of battery management systems may require internal monitoring of lithium ion batteries. In this work, we present an analysis of the interaction between ?ber-optic evanescent wave sensors (FOEWS) and graphite particles within a lithium ion battery over multiple cycles. Through slow charging and long rest periods, the FOEWS signal has shown sensitivity to lithium concentration at the surface of graphite particles by demonstrating a response to the slow di?usion of lithium ions within graphite particles during rest periods (i.e. relaxation of the electrode). The slope of the FOEWS signal during a full charge is found to exhibit three distinct peaks that occur within lithiated graphite's stage transitions zones IV, III and II. Deviation from the observed three peak trend correlates with signi?cant battery capacity fade and thus indicate the sensors ability to detect capacity fade in real-time. During experiments, signi?cant deviations in the slope during charging occurred at about ~5% SOC and minor disturbances to the slope were observed at ~80% SOC, which supports limiting the depth of charge and discharge to avoid accelerated capacity fade. These results deepen our understanding of the FOEWS's interaction with lithium ion batteries and supports the development of algorithms that optimize the control and monitoring of graphite lithiation with the aim of achieving safer operation as well as maximizing capacity and battery life.
关键词： Optical ?ber sensor,Lithium ion battery,State of charge,Graphite,Capacity fade,Signal analysis
更新于2025-09-19 17:13:59
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New Techniques for Sizing Solar Photovoltaic Panels for Environment Monitoring Sensor Nodes
摘要： The development of perpetually powered sensor networks for environment monitoring to avoid periodic battery replacement and to ensure the network never goes offline due to power is one of the primary goals in sensor network design. In many environment-monitoring applications, the sensor network is internet-connected, making the energy budget high because data must be transmitted regularly to a server through an uplink device. Determining the optimal solar panel size that will deliver sufficient energy to the sensor network in a given period is therefore of primary importance. The traditional technique of sizing solar photovoltaic (PV) panels is based on balancing the solar panel power rating and expected hours of radiation in a given area with the load wattage and hours of use. However, factors like the azimuth and tilt angles of alignment, operating temperature, dust accumulation, intermittent sunshine and seasonal effects influencing the duration of maximum radiation in a day all reduce the expected power output and cause this technique to greatly underestimate the required solar panel size. The majority of these factors are outside the scope of human control and must be therefore be budgeted for using an error factor. Determining of the magnitude of the error factor to use is crucial to prevent not only undersizing the panel, but also to prevent oversizing which will increase the cost of operationalizing the sensor network. But modeling error factors when there are many parameters to consider is not trivial. Equally importantly, the concept of microclimate may cause any two nodes of similar specifications to have very different power performance when located in the same climatological zone. There is then a need to change the solar panel sizing philosophy for these systems. This paper proposed the use of actual observed solar radiation and battery state of charge data in a realistic WSN-based automatic weather station in an outdoor uncontrolled environment. We then develop two mathematical models that can be used to determine the required minimum solar PV wattage that will ensure that the battery stays above a given threshold given the weather patterns of the area. The predicted and observed battery state of charge values have correlations of 0.844 and 0.935 and exhibit Root Mean Square Errors of 9.2% and 1.7% for the discrete calculus model and the transfer function estimation (TFE) model respectively. The results show that the models perform very well in state of charge prediction and subsequent determination of ideal solar panel rating for sensor networks used in environment monitoring applications.
关键词： battery state of charge,environment monitoring,solar radiation,discrete calculus model,transfer function estimation,solar photovoltaic panels,sensor nodes
更新于2025-09-11 14:15:04
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Estimation of state of charge of lithium-ion battery based on photovoltaic generation energy storage system
摘要： The fast and accurate estimation of state of charge (SOC) of lithium-ion battery is one of the key technologies of battery management system. In view of this nonlinear dynamic system of lithium battery, through the test and analysis of lithium-ion battery hysteresis characteristics, the second-order RC hysteresis model is established, and the cubature Kalman filter algorithm is used to estimate the battery state of charge in this report. The experiment results show that the battery model can essentially predict the dynamic hysteresis voltage behavior of the lithium-ion battery and cubature Kalman Filtering algorithm can maintain high accuracy in the estimation process.
关键词： cubature Kalman filter,Lithium-ion battery,state of charge,hysteresis model
更新于2025-09-09 09:28:46