#SOC measurement for dry batteries

[semcoinfratechworld]

A Comprehensive Exploration of Battery State of Health (SOH) Estimation

In the complex world of batteries, the State of Health (SOH) is a crucial parameter determining a battery's overall well-being and remaining useful life. Equally important is the State of Charge (SOC) estimation, especially when it comes to SOC estimation for dry goods batteries, where accurate monitoring can significantly influence battery performance and longevity. Advancements in precise SOC estimation technology have made it easier to optimize the state of charge, ensuring maximum efficiency.

Accurate estimation of both SOH and SOC is essential for maximizing battery performance, refining charging strategies, and ensuring long-term reliability. This article explores various methods for estimating SOH, while also addressing how battery state of charge monitoring plays a pivotal role in this process. We will highlight the strengths, limitations, and emerging trends in SOC measurement for dry batteries and SOH that promise to revolutionize our understanding of battery health.

Cycle Counting: Traditional Approach

Cycle counting is a traditional method for estimating SOH. It assumes that a battery's capacity gradually degrades with each charging and discharging cycle. This method provides a straightforward estimate of SOH by keeping track of the total number of cycles. While relatively simple to implement, it also serves as an initial method in SOC estimation techniques.

However, cycle counting has limitations. It oversimplifies the complex factors influencing battery degradation, such as depth of discharge (DOD), temperature variations, and charging patterns. This simplistic approach may lead to inaccurate estimations, especially with modern usage patterns involving partial charging and discharging cycles. As such, advancements in SOC estimation are critical in complementing this traditional method for better accuracy.

Charging Capacity Analysis: Precision Meets Complexity

Charging capacity analysis takes a more dynamic approach to SOH estimation by analyzing the actual charging capacity of the battery. This method compares the energy stored during a charging cycle with the original capacity, providing a more accurate assessment of both battery health and SOC algorithms for batteries.

While charging capacity analysis considers various factors impacting battery performance, it comes with its challenges. Precise measurement often requires sophisticated equipment or accurate SOC estimation methods, increasing implementation costs and complexity. Moreover, its accuracy is highest when the battery is charged from a low state of charge (SOC) to a fully charged state, potentially underestimating capacity decline with frequent charging from higher SOC levels.

Combining Cycle Counting with Charging Capacity Analysis

Recognizing the limitations of individual methods, a contemporary trend in SOH estimation involves combining cycle counting with charging capacity analysis. This synergistic approach aims to comprehensively evaluate battery degradation, considering both cumulative cycles and dynamic variations in charging behaviors and environmental conditions. Real-time SOC estimation for batteries and innovations in battery SOC tracking is integral to this comprehensive evaluation.

Emerging Trends in SOH and SOC Estimation

Machine Learning (ML): Precision and Dynamism: Incorporating machine learning algorithms trained on extensive battery data has emerged as a game-changer. ML goes beyond traditional methods, considering factors beyond cycle count and charging capacity. This approach enables more accurate and dynamic predictions of SOH and SOC prediction advancements.

Electrochemical Impedance Spectroscopy (EIS): Unveiling Internal Dynamics: EIS, a technique analyzing the battery’s internal resistance, offers insights into its health and facilitates early detection of potential degradation issues. It’s a key element in improving battery state of charge monitoring and overall, SOC improvement for dry goods batteries.

Open-Circuit Voltage (OCV) Analysis: Monitoring the Unseen: OCV analysis involves monitoring the battery’s open-circuit voltage during charging and discharging cycles, providing valuable information about its health and remaining capacity. This method adds another layer of precision to the SOH estimation process and can further support the battery management system SOC.

Conclusion: Navigating Towards Precision in SOH and SOC Estimation

In the ever-evolving landscape of battery technology, precise estimation of both the State of Health and State of Charge is crucial. By understanding the strengths and limitations of conventional methods like cycle counting and charging capacity analysis, coupled with embracing emerging techniques such as machine learning, EIS, and OCV analysis, we pave the way for a comprehensive understanding of battery health and charge.

Ongoing advancements in SOC estimation and battery SOC prediction advancements hold immense promise for enhancing the accuracy and reliability of SOH and SOC estimation, ultimately optimizing battery performance, lifespan, and sustainability in the long run.

[peterwilson06523]

Mastering The Art Of Storing Lithium Batteries: Best Practices For Longevity And Safety

Lithium batteries have become ubiquitous in the modern lives, powering everything from smartphones and laptops to electric vehicles and renewable energy storage systems. However, proper storage is crucial to ensure the longevity and safety of lithium batteries. Here are some best practices for storing lithium batteries:

Temperature Control: 

Lithium batteries should be stored at moderate temperatures to maintain their performance and longevity. Extreme heat can accelerate the degradation of the battery cells, while extreme cold can reduce their capacity and cause damage. Ideally, lithium batteries should be stored in a cool, dry place with temperatures ranging between 15°C to 25°C (59°F to 77°F).

Avoiding Direct Sunlight:

Exposure to direct sunlight can cause lithium batteries to overheat, leading to thermal runaway and potentially hazardous conditions. Store lithium batteries in a location away from direct sunlight, such as a closet or drawer, to prevent excessive heat buildup.

Proper Ventilation: 

Adequate ventilation is essential when storing lithium batteries, especially in confined spaces. Lithium batteries can release gases during charging or discharging, and proper ventilation helps dissipate these gases to prevent the buildup of pressure inside the storage area.

Avoiding Moisture: 

Moisture can corrode the terminals of lithium batteries and compromise their performance. Store lithium batteries in a dry environment, away from sources of moisture such as sinks, bathrooms, or basements prone to flooding. Consider using airtight containers or silica gel packs to absorb excess moisture and protect the batteries.

Individual Packaging: 

When storing multiple lithium batteries, it's essential to package them individually to prevent short circuits or accidental discharge. Use plastic cases or storage containers designed specifically for lithium batteries to keep them separated and protected from physical damage.

Charge Level: 

Lithium batteries should be stored at a partial state of charge (SOC) rather than fully charged or fully depleted. Aim for a charge level between 30% to 50% to minimise stress on the battery cells while still retaining enough charge for future use. Avoid storing lithium batteries at full charge for extended periods, as this can accelerate degradation and reduce their overall lifespan.

Regular Monitoring: 

Periodically check the condition of stored lithium batteries to ensure they remain in good working order. Inspect for any signs of physical damage, leakage, or swelling, which may indicate a potential safety hazard. If any abnormalities are detected, safely dispose of the affected batteries following proper recycling procedures.

Fire Safety Precautions: 

In the event of a lithium battery fire, it's essential to have appropriate fire safety measures in place. Store lithium batteries away from flammable materials and have fire extinguishing equipment readily available, such as a Class D fire extinguisher designed for lithium battery fires. Additionally, consider installing smoke detectors and fire alarms in storage areas for early detection of potential hazards.

By following these best practices for storing lithium batteries, you can maximise their lifespan, maintain their safety, and ensure optimal performance when they are needed. Whether storing spare batteries for electronic devices or backup power supplies for emergencies, proper storage is essential for getting the most out of your lithium batteries.

[csfighting]

A Scanning Device that measures the water content of soil.

Common Structure types followed by the papers.

As what I have observed from the articles and research paper that I have read, is that most of them starts with an abstract which they put a summary or a brief discussion of what there article or research is all about. They have an introduction that talks about their topic for the research what are the problems they have encountered that lead them to do their research. After the introduction it was followed by the materials and methods, in this part they show about what are the particular methods that they have used to in order to calculate the results. Also, they have the model design in which they describe the features of their model. Next, is the simulation or the testing stage it is where they test device or model its specific area for them to know what would be the result of their research. They also have the results and discussion which they gather all of their results and findings, and they give their thoughts and insights about the problem after undergoing through rigorous test.  Lastly, they have the conclusion which it will be representation of the solution that you want to achieve.

Definitions and concepts in the article that are not familiar. Topic was all about soil and its kind of measurements.

Direct method – Direct measurements of soil water content involve removing water from a soil sample by evaporation, leaching or chemical reaction. The soil moisture content is calculated from the mass of water removed and the mass of the dry soil.

Indirect method – Indirect methods involve measurement of some property of the soil that is affected by soil water content. Indirect methods can also measure a property of some object placed in the soil. Instrumental method, which measure some other property that is related to soil moisture, allow you to monitor soil moisture over time, at various depths, and to automate data collection. No water content in the soil is directly measured but the water potential or stress

Salinization - refers to a buildup of salts in soil, eventually to toxic levels for plants.  When soils are salty, the soil has greater concentrations of solute than does the root, so plants can't get water from soil. The salts can also be directly toxic, but plant troubles usually result primarily from inability to take up water from salty soils.

Hydraulic Conductivity – Saturated hydraulic conductivity is a quantitative measure of a saturated soil's ability to transmit water when subjected to a hydraulic gradient. It can be thought of as the ease with which pores of a saturated soil permit water movement.

In situ measurement - require that the instrumentation be located directly at the point of interest and in contact with the subject of interest.

Intel Galileo Gen 2 processor -  The Intel® Galileo Gen2 is a board based on the Intel® Quark™ SoC X1000, a 32-bit Intel® Pentium® processor-class system on a chip (SoC), operating at speeds up to 400MHz. The board is designed to be hardware and software pin-compatible with Arduino shields designed for the Uno R3. Digital pins 0 to 13 (and the adjacent AREF and GND pins), Analog inputs 0 to 5, the power header, ICSP header, and the UART port pins (0 and 1), are all in the same locations as on the Arduino Uno R3.

References:

Theygesen, L. “Soil Moisture Meters” March 19, 2004 Retrieved February 7, 2018 <http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/eng8291/>

Bitteli, M. “Measuring Soil Water Content” A Review, June 2011 vol. 21 no. 3293-300 Retrieved February 7, 2018 <http://horttech.ashspublications.org/content/21/3/293.full/>

“Saturated Hydraulic Conductivity” Water Movement Concepts and Class History <https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_053573/>

“ Intel® Galileo Gen 2 Board Product Specifications “ <https://www.arduino.cc/en/ArduinoCertified/IntelGalileoGen2/>

CLAIMS FROM THE ARTICLES/RESEARCH PAPERS

Determination of Soil Suitability for Agriculture Farming Using Microwave Analysis

1.    Comparison Between Literature And Calculated Dielectric

The error between the literature dielectric constant and calculated dielectric constant has been shown. There is minor error between the calculated effective dielectric constant of the soil samples with particular moisture % and the effective dielectric constant values collected from literature.

2.   Model Of The Soil Dielectric Verses Moisture Content

 By putting the values of dielectric constant of soil in the model equation (8), the estimate % moisture content in soil is determined. Based on the collected data about the % moisture content in soil or loamy soil, in a region or farm, the suitable agriculture crops are recommended for the better results.

 Design and Development of M3SS: A Soil Sensor Node for Precision Agriculture

 1.     The current consumption of M3SS sensor node is compared with different sensor nodes as shown in table 3.The current consumption is measured during three different stages, that is, sleep duration, active duration and transmission duration. In sleep duration, sensing unit and communication unit are powered off mode and processor is in sleep mode. During active duration, sensing unit and processor is active but communication unit is in off state. Only communication unit is active in transmission duration and processor is in sleep mode with sensing unit is power off.

 2.     A 20W solar panel with D.C. converter of 1.5 amp and lead acid battery of 6.5 Ah battery is used to provide power to M3SS. A battery charging and cutoff circuit is also inserted between solar panel and battery so that complete charging and discharging cycle of battery occur. The cost factor of M3SS sensor node is also compared with different sensor nodes as shown in table 4.The price of an agricultural tool or equipment or system is a very important parameter for the farmers because they cannot afford expensive equipment. The M3SS has low price as compared to other nodes. Therefore, M3SS node should be used for agriculture application.

Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness

 1.       Compared to in situ observations, MiOFA improved the VWC retrieval skill. However, the MiOFA may have overestimated VWC. Possible reasons of this overestimation are the heterogeneity of VWC in the satellite footprint scale [see whiskers and differences between observations of different types in Fig. 7(a)], the uncertainty of the b-parameter and single scattering albedo as discussed in Section III-D, and the simplification of the canopy radiative transfer using the zero-order model. It should be noted that since the number of in situ VWC observations is small, no statistical metrics were calculated. Fig. 7(b) and (c) shows that the roughness correction by the MiOFA strongly affects the SSM retrieval.

   The Development of Soil Water Content Detector

 1.     Fig. 3 consists of 24 serial data which were taken every 5 seconds within 2 minutes. The obtained data on Fig. 3 is the data taken using YL-69 sensor, a while after the water content was added. The value to compare was the value of Water Content which was correlated with ADC (Analog to Digital Converter) Reading. ADC is the analog input changer from the sensor to be digital codes in a computer system. According to the testing data mean of soil water content change, it then provides the arched exponential curve to bottom right. That curve shows that the greater water content measures, the lower ADC to display with the gradual change value. Compared to the theory, it proves that the more humid soil, the lower resistance produced and the current is greater to pass through.

REFERENCES:

Solanki, L. S., Singh, S., and Garg, N. “Determination of soil suitability for agriculture farming using microwave analysis” 27 July 2017 Retrieved February 7, 2018 < <ieeexplore.ieee.org/abstract/document/7993820/ />

Kumar, P., Reddy, S.R.N. “Design and development of M3SS” A Soil Sensor Node for precision agriculture 08 June 2017 Retrieved February 7, 2018 <http://ieeexplore.ieee.org/document/7939520/>   

 Sawada, Y., Koike, T., Aida, K., Toride, K., and Walker, J. P. “Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness” 11, NOVEMBER 2017 Retrieved February 7, 2018 <http://ieeexplore.ieee.org/document/7993040/>

 Suharjono, A., Mukhlisin, M., and Alfisyahrin, N. K. H. “The Development of Soil Water Content Detector” Oct 18-19, 2017 Retrieved February 7, 2018 < http://ieeexplore.ieee.org/document/8257694/ />

[vagabondphotog]

Summary Thoughts on Dry Camping

After dry camping in the Outer Banks for nearly 2 weeks, with only 1 camp departure to dump tanks and restock, here are my thoughts, in no particular order.

1.  I need to get a battery monitor.  If I’m going to rely on battery power, and occasional use of the generator, I need a better way to determine the state of charge (SOC) of the batteries.  As it stands, the only way I have to determine the battery’s SOC is to measure the open circuit voltage and compare that to a chart that indicates the charge left in the battery, based on the voltage reading.  The only problem is that there’s no easy way to measure the open circuit voltage.  The MotorHome has several battery voltage meters, but they are measuring the battery voltage at that moment in time, under what ever loads may be drawing out of the battery.  I can go to the extreme of shutting off lights and any other obvious draw on the battery, but that’s still not an accurate way to measure the true open circuit voltage, since there are always vampire loads drawing power.  The refrigerator is a good example.  Even while it’s running on propane, the electronic circuitry is drawing 12 volt power, and there’s a small air circulation fan behind the unit that’s also drawing power.  

A battery monitor uses a shunt to measure the current draw (out of or into) the battery and logs that over time to create an estimate of the actual amp-hours that have been drawn out of (or, in the case of charging, sent into) the battery.  It also uses current flow to determine when a 100% SOC has been reached for the battery by determining when the battery float voltage has been reached and the charging current going into the battery has reached the “tail” current. Right now, I’m looking at the Victron BMV-700 as my leading choice.  

Yes, I’ve learned a lot about batteries in the past couple of weeks!  

2.  I can live very comfortably for a week on a full tank (80 gallons) of fresh water.

3.  The propane tank goes from reading Full to 3/4 full almost immediately.  I think that’s because “Full” counts the vapor space in the tank.  The tank went from Full to 3/4 after only about 1 day of usage.  But it took an additional 5 days for it to go from 3/4 to 1/2.

4.  This one isn’t so much about dry camping as it’s about the Outer Banks in November.  Of the 12 days I was there, there were sustained winds over 15 mph about 7 of those days.  That’s not pleasant when the temperature is in the upper 50s/lower 60s.  Now I understand why the Wright brothers chose December in the Outer Banks to try out their “flying machine” idea!

5.  Related to item 1, I’d also like to get some solar panels.....maybe start with a 200 watt set of portable panels.  While the generator worked fine, charging the batteries passively and quietly with solar power would be a great addition to the dry camping set up.  A 200 watt set of portable panels, including a solar charge controller, costs about $450.  

6.  I need to get a portable 20 lb BBQ grill propane tank.  I’ve been using the green 1 lb cyclinders to run the Weber grill and the Little Red Campfire in a can. This is a hugely expensive way to purchase and use propane.  One of those little green bottles only lasts about 3 days on the grill with constant daily use of the grill.  

7.  I have to say though, after dry camping for 12 days, it’s very nice to return to “civilization” where I can plug into 50 amp electric service with unlimited water, cable television and internet!  I don’t think a steady diet of dry camping for weeks on end is for me.  

But, in spite of these ideas for increasing my comfort and sustainability while dry camping, the overall verdict is that dry camping for a week or two, interspersed with periods of full hookups, is perfectly fine.

[semcoinfratechworld]

Advancements in Precise State of Charge (SOC) Estimation for Dry Goods Batteries

In the dynamic world of dry goods batteries, accurately determining the State of charge estimation (SOC estimation for dry goods batteries) is crucial for optimal performance and longevity. This article explores two widely used methods for SOC estimation for dry goods batteries: the Anshi integral method and the open-circuit voltage method. By examining their mechanics, strengths, and limitations, we aim to understand each method's suitability for different battery types clearly, highlighting recent advancements in SOC estimation.

I. The Anshi Integral Method

The Anshi integral method precisely calculates SOC by considering critical variables such as charge and discharge currents, time, and total capacity. This method is a cornerstone of Precise SOC estimation technology and is versatile and suitable for various battery chemistries.

Operational Mechanics

Current Measurement: Accurate measurements of charge and discharge currents using high-precision sensors are fundamental to SOC measurement for dry batteries.

Time Integration: Integrating measured currents over time to determine the total charge transferred utilizes advanced SOC algorithms for batteries.

SOC Calculation: Dividing the total charge transferred by the battery's capacity to obtain SOC ensures Accurate SOC estimation methods.

Strengths

Versatility: Applicable to different battery chemistries, enhancing Dry goods battery SOC improvement.

Robustness: Resilient to noise and parameter variations, supporting reliable Battery state of charge monitoring.

Accuracy: Provides precise SOC estimation when combined with other methods, contributing to Improving SOC estimation accuracy.

Limitations

Sensor Dependence: Accuracy relies on the quality of current sensors, affecting overall Battery management system SOC.

Temperature Sensitivity: SOC calculation can be affected by temperature variations, necessitating adaptive measures.

Computational Complexity: The integration process can be computationally expensive, impacting real-time applications.

II. The Open-Circuit Voltage Method

The open-circuit voltage method estimates SOC by measuring a battery's voltage when no load is connected. This method is particularly effective for ternary and lithium manganate batteries due to their unique voltage characteristics, representing significant Innovations in battery SOC tracking.

Operational Mechanics:

Voltage Measurement: Measuring the battery's open-circuit voltage is a fundamental aspect of State of charge estimation techniques.

SOC Lookup Table: Comparing the measured voltage to a pre-constructed lookup table utilizes Battery SOC prediction advancements.

SOC Determination: Obtaining the corresponding SOC value from the lookup table ensures reliable Real-time SOC estimation for batteries.

Strengths:

Simple Implementation: Requires minimal hardware and computational resources, making it an Accurate SOC estimation method.

High Accuracy: Provides precise SOC estimates for specific battery chemistries, enhancing SOC measurement for dry batteries.

Temperature Independence: Relatively unaffected by temperature variations, improving overall SOC estimation accuracy.

Limitations:

Limited Applicability: Effective only for batteries with well-defined voltage-SOC relationships, restricting its use.

Lookup Table Dependence: Accuracy depends on the quality and completeness of the lookup table, highlighting the need for comprehensive data.

Dynamic Voltage Fluctuations: Self-discharge and other factors can affect open-circuit voltage accuracy, challenging State of charge estimation.

III. Suitability for Different Battery Types

The open-circuit voltage method is generally applicable, but its accuracy varies depending on the battery chemistry:

Ternary Batteries: Highly suitable due to distinct voltage-SOC relationships.

Lithium Manganate Batteries: Performs well due to stable voltage profiles.

Lithium Iron Phosphate Batteries: Requires careful implementation and calibration for accurate estimation within specific SOC segments.

Lead-Acid Batteries: Less suitable due to non-linear voltage-SOC relationships.

IV. Factors Affecting State of Charge Calculation

Several factors influence SOC estimation accuracy:

Current Sensor Quality: Accuracy depends on high-precision sensors, critical for Battery state of charge monitoring.

Temperature Variations: Battery capacity changes with temperature, affecting SOC calculation.

Battery Aging: Aging reduces capacity and increases internal resistance, impacting SOC accuracy.

Self-discharge: Natural discharge over time can lead to underestimation of SOC.

Measurement Noise: Electrical noise in the system can introduce errors in SOC calculation.

V. Enhancing SOC Estimation Accuracy

To achieve accurate SOC estimation, several strategies can be employed:

Fusion of Methods: Combining the Anshi integral method with the open-circuit voltage method improves accuracy by leveraging dynamic and static information, representing key Advancements in SOC estimation.

Adaptive Algorithms: Real-time data-driven algorithms compensate for changing battery parameters and environmental conditions, enhancing SOC algorithms for batteries.

Kalman Filtering: Advanced filtering techniques reduce measurement noise, enhancing accuracy and reliability.

VI. Impact of Accurate SOC Estimation

Accurate SOC estimation has significant implications across various applications:

Optimized Battery Usage: Avoiding overcharging and deep discharging extends battery life and enhances performance, contributing to Dry goods battery SOC improvement.

Improved Safety: Reliable information on remaining capacity prevents safety hazards associated with improper charging or discharging.

Extended Battery Lifespan: Minimizing stress on batteries prolongs their lifespan, reducing costs and environmental impact.

Efficient Battery Management: Accurate SOC information enables optimized charging, discharging, and prevention of premature failure, integral to Battery management system SOC.

VII. Applications in Various Industries

Accurate SOC estimation finds applications beyond dry goods batteries:

Renewable Energy Systems: Optimizes energy storage in solar and wind power installations.

Electric Vehicles: Predicts driving range and optimizes battery performance, leveraging Battery SOC prediction advancements.

Portable Electronics: Provides reliable information on remaining battery life in smartphones and laptops.

Medical Devices: Ensures reliable operation of battery-powered medical devices for patient safety.

VIII. Future Development

Advancements in SOC estimation can be expected in the following areas:

Advanced Machine Learning Techniques: Analysing data patterns for even greater accuracy.

Battery Health Monitoring Integration: Comprehensive insights into battery performance and failure prediction.

Wireless Communication: Real-time monitoring and remote battery management, enhancing Real-time SOC estimation for batteries.

Conclusion

Accurately estimating State of charge estimation is crucial for optimizing dry goods battery performance and lifespan. Understanding the mechanics, strengths, and limitations of the Anshi integral method and the open-circuit voltage method allows informed selection and implementation for different battery types. As technology progresses, further advancements in SOC estimation techniques will enhance the efficiency and reliability of dry goods batteries across diverse applications, driving forward Innovations in battery SOC tracking and Battery SOC prediction advancements.