Li-ion Battery Life cycle

EFFECT OF OVERCHARGED LI-ION BATTERY CELL LIFE CYCLE

Abstract:

To analyse the impact of electrical abuse on Li-ion battery by overcharging which causes degradation anode, cathode and seperator layers, this study thoroughly investigates the high current overcharge effect and degradation of Li-ion batteries (LIBs).This project investigates the effects of overcharging on a specific type of lithium-ion battery, namely the 18650 cell with a capacity of 2400 mAh. The behavior of the battery cell is examined by subjecting it to an overcharging process using a data acquisition setup. The collected data is then extrapolated and analyzed. During the high current cycling process, battery health decreases significantly. Besides, the active material decreases when the battery degrades to a certain level. Degradation of layers is the primary reason for loss in battery health. The plated lithium grows with the increment of degraded/overcharged level. Besides, the dissolution and deposition affect the internal short circuit degree, which can be observed from the electrode potential and cell voltage value. Moreover, battery cells undergo different degradation degrees, and different current rates of charging/discharging exhibit similar temperature-rising trends progresses to thermal runaway(TR) . However, with the degradation degree increase, battery capacity fades, TR becomes easier to be triggered by the high current rate, and TR reactions are severe. The results of this study contribute to a better understanding of the impact of overcharging on lithium-ion batteries this study focuses on Life cycle estimation for overcharged lithium-ion battery cell in any electronic systems

Data Acquisition setup

Data acquisition for studying the effect of thermal abuse in overcharged Li-ion batteries typically involves measuring and recording various parameters during the testing process. These parameters can include:

- Temperature: The battery's temperature is a critical parameter to monitor during thermal abuse testing. Temperature sensors, such as thermocouples or infrared cameras, can be used to measure the temperature at different points on the battery.
- Voltage: The battery's voltage is monitored to determine its behaviour during thermal abuse. Voltage measurements can be taken using a data acquisition system (DAQ) or multimeter.
- Current: The battery's current is measured to determine how much power is being drawn from the battery during thermal abuse. This can also be measured using a DAQ or multimeter.
- Time: The duration of the thermal abuse testing is also recorded to track the battery's behaviour over time.

Other parameters: Other parameters that may be monitored during the testing process include pressure, gas emissions, and changes in the battery's physical dimensions.
To acquire the data, sensors and measurement equipment are typically connected to a DAQ system, which collects, stores, and analyses the data. The DAQ system can be programmed to measure and record the data at specific intervals and can provide real-time feedback on the battery's behaviour during testing. Once the data is collected, it can be analysed using software tools to identify patterns and trends and draw conclusions about the battery's behaviour during thermal abuse.
When a battery is connected to a bi-directional buck-boost converter with an FPGA (Field-Programmable Gate Array), it allows for more precise control of the power flow between the battery and the load. The bi-directional buck-boost converter can either step up or step down the voltage level of the battery to match the requirements of the load. This means that the converter can be used to charge the battery from a lower voltage source or to discharge the battery to a higher voltage load.

The FPGA is used to control the switching of the converter and can be programmed to optimize the power flow between the battery and the load. It can also be used to implement different control algorithms, such as maximum power point tracking (MPPT), which can help to maximize the efficiency of the system.
The FPGA can also be used to monitor and control other aspects of the battery system, such as battery voltage, current, and temperature. This information can be used to ensure that the battery is operating within safe limits and to implement protection mechanisms, such as overcurrent and overvoltage protection.

Data Acquisition setup

Components:

FPGA Controller:
An FPGA controller refers to a controller implemented on an FPGA chip that can be programmed to perform a variety of functions. These controllers are typically used in digital control systems where a high degree of customization and flexibility is required.
FPGA controllers can be designed to interface with a variety of sensors, actuators, and other devices in a system, allowing for real-time control and monitoring. They can also be programmed to implement various control algorithms, such as proportional-integral-derivative (PID) controllers, which can be used to regulate system behaviour.
One of the advantages of using an FPGA controller is the ability to implement hardware-level parallelism, which allows for faster and more efficient processing of data. Additionally, FPGAs can be programmed and reprogrammed in real-time, allowing for rapid prototyping and testing of control algorithms.
FPGA controllers can be used in a wide range of applications, including robotics, industrial automation, power electronics, and aerospace. They offer a high degree of flexibility and customization, making them well-suited for complex control systems.
The use of FPGA controllers is not without its problems, though. The FPGA's programming, which can be a difficult and time-consuming operation, is one of the key difficulties. In addition, FPGA controllers are frequently more expensive than conventional microcontrollers, which may limit their use by amateurs and smaller businesses.
In general, FPGA controllers provide a strong and adaptable solution for a variety of applications. FPGA controllers are becoming more and more common in industries like robotics, autonomous cars, and aerospace systems due to their ability to process complicated computations fast and effectively. FPGA controllers are a useful tool for many applications, despite the fact that using them comes with some difficulties.

FPGA Controller

BI-DIRECTIONAL BOOST CONVERTER:
A bi-directional boost converter is a type of power electronic converter that can either step up or step down the voltage of a DC power source to match the requirements of a load or a storage device. It is called bi-directional because it can operate in both the buck and boost modes.
In the boost mode, the converter steps up the input voltage to a higher output voltage, which is useful for charging batteries or powering loads that require a higher voltage than the input source. In the buck mode, the converter steps down the input voltage to a lower output voltage, which is useful for discharging batteries or powering loads that require a lower voltage than the input source.
The bi-directional boost converter is often used in applications where bidirectional power flow is required, such as in energy storage systems, electric vehicles, and renewable energy systems. The converter can be controlled using various control strategies, such as pulse width modulation (PWM) or maximum power point tracking (MPPT), to ensure efficient operation and optimal power transfer. Overall, the bi-directional boost converter is a versatile and efficient power electronic converter that can be used in a wide range of applications where bidirectional power flow is required.

BI-DIRECTIONAL BOOST CONVERTER

DC POWER SUPPLY:
A DC (direct current) supply is an electrical power supply that provides a constant DC voltage to power electronic devices or circuits. DC power supplies are commonly used in electronic devices such as computers, televisions, and mobile phones, as well as in industrial and scientific equipment.
A DC supply typically consists of a rectifier that converts AC (alternating current) power from the mains into DC power, followed by a smoothing capacitor that filters the DC voltage and removes any ripple or noise in the output. The output voltage of a DC supply can be fixed or adjustable, depending on the application.
DC power supplies can be classified into two main types: linear and switching. Linear power supplies use a linear regulator to maintain a constant output voltage, while switching power supplies use a switching regulator to convert the input voltage to the desired output voltage. Switching power supplies are generally more efficient and smaller in size than linear power supplies, but can introduce more noise into the output.Overall, a DC supply is a fundamental component of many electronic systems and is used to provide a stable, regulated DC voltage to power electronic devices or circuits.

DC POWER SUPPLY

18650 Type Lithium-ion Battery cell:
A typical cylindrical battery found in many portable electronic devices, including laptops, power banks, flashlights, and electric cars, is the 18650 type lithium-ion battery cell. Its name refers to its measurements of 65mm in length and 18mm in diameter. The 18650 cell is a well-liked option in the rechargeable battery industry due to its high energy density and reasonably long lifespan.
The lithium-ion chemistry used by the 18650 cell enables it to efficiently store and discharge electrical energy. During charging and discharging cycles, lithium ions flow back and forth between the battery's positive and negative electrodes. The cathode of the 18650 cell can be formed of a variety of substances, including lithium cobalt oxide, lithium manganese oxide, or lithium iron phosphate. The anode of the 18650 cell is commonly made of carbon.
The 18650 cell is suitable for applications that require a lot of power because it can deliver high currents. It can maintain its charge for a longer time when not in use because to its comparatively low self-discharge rate. The 18650 cell needs protection circuitry, like all lithium-ion batteries, to stop overcharging and overdischarging, which can harm the battery and potentially cause it to catch fire or explode.
The adaptability of the 18650 cell is one of its benefits. To produce battery packs with a higher voltage or a greater capacity, it is simply joined in series or parallel. Because of this, DIY enthusiasts who wish to construct custom battery packs for a variety of projects frequently choose it. Additionally, by arranging the cells to achieve the best possible balance between capacity, voltage, and current output, it enables manufacturers to create battery packs that are specifically tailored for a variety of purposes.
The 18650 cell does have some limits, though. For producers of compact electronic gadgets, its cylindrical shape makes it less space-efficient than other battery types, which might be a challenge. Additionally, it has a propensity to become hot while discharging or charging at high rates, which may compromise its longevity and safety. Additionally, the 18650 cell must be handled and stored properly to prevent damage or failure because, like all lithium-ion batteries, it is sensitive to high temperatures.
In summary, the lithium-ion battery cell of the 18650 type is a popular and adaptable battery technology that offers high energy density, high power output, and adaptability. It has uses in many different industries, including consumer electronics, electric cars, and renewable energy sources. The 18650 cell continues to be a popular option for many battery-powered systems and devices despite its drawbacks and safety issues.

BATTERY PACK:
A battery pack configured in a 3S3P arrangement consists of three individual cells connected in series and three sets of these series-connected cells connected in parallel. Each cell has a voltage rating of 3.6 volts and a capacity of 2500mAh. By connecting the cells in series, the total voltage of the battery pack is increased to 10.8 volts, which is advantageous for applications requiring a higher voltage output. On the other hand, connecting the cells in parallel boosts the overall capacity, resulting in a combined capacity of 7500mAh. This configuration offers a balance between voltage and capacity, making it suitable for a wide range of applications. The 3S3P battery pack is commonly utilized in devices like electric vehicles, drones, backup power systems, and high-performance portable electronics. It provides an efficient power solution with increased voltage output, extended runtime, and improved performance for various demanding tasks. Additionally, the parallel configuration enhances the pack's current capability, allowing it to deliver higher currents when required, which is especially useful in applications with high power demands.

EXTRAPOLATION BATTERY HEALT VALUES USING CODE:
This code performs extrapolation on a battery health dataset across a specific number of cycles using four different methods: exponential, logarithmic, K-Nearest Neighbors (KNN), and linear regression. The code imports necessary libraries such as NumPy, Matplotlib, SciPy (for curve fitting), Scikit-Learn (for KNN regressor and linear regression), and Pandas (for reading the dataset from a CSV file).
The battery health data is read from the "Battery2.csv" file and stored in variables called x data and y data. x data represents the cycle numbers, and y data represents the corresponding health values.
Two functions, namely func and logfunc, are defined. The func function takes a cycle number (x) and three coefficients (a, b, c) as input and predicts the battery health at that cycle number based on those coefficients. The logfunc function is similar but takes only two coefficients (a, b) and predicts the health based on a logarithmic relationship with the cycle number.
The curve fit function from SciPy is used to fit the func and logfunc functions to the data, generating sets of coefficients (popt and popt1) that provide the best fit. Four extrapolation methods are then applied to predict future battery health values beyond the original data range. The func function with the popt coefficients is used to predict health values for cycle numbers beyond the original range (e.g., cycles 1100 to 1400). Similarly, the logfunc function with the popt1 coefficients, a KNN regressor, and a linear regression model are used to make predictions for the same cycle numbers.
The predicted health values from each extrapolation method are plotted using Matplotlib and saved as a PNG file named "Health pred.png". The x-axis represents the cycle number, and the y-axis represents battery health. The original data is shown in red, while the extrapolation methods are represented by different colors (green, blue, purple, and yellow). A legend is included to indicate which line corresponds to each extrapolation method.

VALIDATION OF CODE FOR EXISTING DATA SET:
The figures presented offer valuable insights into the battery's behavior over time. Figure demonstrates a gradual decrease in the battery's state of health (SOH) as the number of cycles increases. The initial SOH value of approximately 100 decreases to around 99 at cycle 100, further declining to approximately 98 at cycle 200. The downward trend continues, reaching approximately 95.5 at cycle 490. This information is essential for understanding the battery's performance and can contribute to the development of more efficient and reliable battery technologies.
Furthermore, Figure reveals that at cycle 490, the state of charge (SOC) value is approximately 95.5. This indicates that the battery is nearly fully charged at this point in the cycle, providing valuable insights into the battery's behavior over time. Such information is beneficial for predicting battery performance and designing optimized battery technologies.
Figure displays the extrapolated data obtained from the code. Although the original dataset only included values up to 300 cycles, running the code allows us to observe the SOC value for cycle 490, which is approximately 95.5. This expanded dataset provides a significant increase in available data, enabling more accurate predictions about the battery's behavior.
In conclusion, these figures offer significant insights into the battery's behavior over time and demonstrate the effectiveness of the code in extrapolating data beyond the original dataset's range. The information derived from these figures can drive improvements in battery design, performance, and optimization for various applications.
The code has demonstrated its ability to accurately extrapolate data for the battery, even beyond the range of the original dataset. This capability is crucial for gaining insights into the long-term behavior and performance of the battery. The use of multiple extrapolation methods, including exponential, logarithmic, K-Nearest Neighbors (KNN), and linear regression, further enhances the accuracy and versatility of the code. By leveraging the strengths of each method, a comprehensive analysis and prediction of the data can be achieved.
The code's ability to plot the predicted values alongside the initial data in a clear and concise manner provides a visual representation of the extrapolation accuracy. This enables easy comparison and interpretation of the results and helps identify any potential discrepancies or outliers.
Overall, the successful extrapolation of data using the code demonstrates its utility and relevance for predicting the battery's performance beyond the available dataset. This has significant implications for the development of more efficient and reliable battery technologies by providing valuable insights into the battery's long-term behavior and performance.

RECORDINGS AND MEASUREMENTS FOR GOOD AND DAMAGED BATTERY CONDITIONS:
The utilization of our data acquisition setup enabled us to amass an extensive amount of data pertaining to battery performance. Specifically, we gathered information from 150 charging and discharging cycles, encompassing both intact and mechanically damaged battery conditions. This dataset is of utmost importance in comprehending the behavior of batteries across different scenarios and over prolonged durations.
The data points we captured consist of significant parameters such as the state of health (SOH), current capacity, energy capacity, charging time, and discharge time. These parameters offer invaluable insights into battery performance, allowing us to identify any existing issues or potential areas for enhancement.
The state of health (SOH) emerges as a critical metric for evaluating battery well-being and longevity. It quantifies the overall health of the battery by expressing it as a percentage of its original capacity. A lower SOH reading indicates suboptimal performance, potentially necessitating battery replacement.
Current capacity and energy capacity provide valuable information regarding the battery's charge-holding ability and energy output. These parameters are pivotal in predicting the battery's longevity under various conditions and play a vital role in optimizing battery performance.
Charging time and discharge time are equally crucial metrics as they shed light on the duration required for charging and discharging operations. This knowledge allows us to optimize battery utilization and determine the most efficient charging and discharging protocols.
The data obtained from our diligent acquisition setup serves as a rich source of information on battery performance and assumes a crucial role in the advancement of battery technology. Through careful analysis of this data, we can deepen our understanding of battery mechanics and develop battery technologies that are both more efficient and reliable.

RECORDINGS AND MEASUREMENTS FOR GOOD AND DAMAGED BATTERY CONDITIONS :

Good Battery Results:

Overcharged Battery Results:

EXTRAPOLATED DATA FROM THE HEALTHY BATTERY:
The dataset provided to us only includes 150 cycles of battery charging and draining. However, the implemented algorithm proved successful in extrapolating the data and forecasting the battery's behaviour for up to 2000 cycles. This is a significant accomplishment since it allows us to assess the battery's health beyond the current experimental data. By projecting the data, we can gain an estimate of the battery's behaviour over a longer length of time, which is valuable in estimating the battery's long-term health and performance.
The code's success in projecting the data to 2000 cycles demonstrates that it was created using solid and dependable algorithms capable of accurately modelling the battery's behaviour. This information is useful in establishing the battery's long-term viability and optimising its performance over time. The capacity to forecast the behaviour of the battery beyond the given data is especially crucial when the battery is intended for long-term usage, such as in electric vehicles or renewable energy systems. Overall, the code's success in projecting the data to 2000 cycles is a positive result with significant implications for the design and optimisation of battery systems.

CONCLUSION:
After conducting extensive testing and analysis on good conditioned battery pack and overcharged battery packs, we have gained valuable insights into their behavior and performance.
Regarding the analysis of data obtained from the battery testing setup, we have identified an inexpensive and reliable method for extrapolating data. We found that the accuracy of extrapolated data varied depending on the condition of the battery. For batteries in good condition, the linear extrapolation method provided the most accurate and expected results.
We found a gradual decrease in the state of health (SOH) of batteries in good condition as the number of cycles increased, with the SOH ranging between 95-96\%. Furthermore, we discovered that the rate of SOH decrease became constant after a considerable number of cycles. This implies that batteries in excellent condition perform consistently over their service life, as the steady drop in SOH has no substantial impact on their overall functionality. These insights are useful for forecasting the long-term behaviour of healthy batteries and optimising their performance.
Our studies on overcharged batteries, on the other hand, demonstrated a continuing decrease in SOH as the number of cycles rose, without hitting a lower bound. Because the overcharged battery was unable to contribute to performance, other cells supplied more current, causing overheating and further damage. As a result, the battery's current capacity reduced to 5Ah from the initial cycle, aggravating the current capacity depletion. These findings are critical for determining the long-term viability of mechanically damaged batteries and making educated decisions about replacing or repairing them.
Overall, the striking disparities in behaviour between excellent and mechanically damaged batteries highlight the importance of precisely analysing battery state before making judgements about their use or replacement. Understanding how batteries behave in diverse environments allows us to improve their performance, extend their lifespan, and reduce the danger of damage or failure. These discoveries help to enhance battery technology and ensure effective and dependable battery utilisation.