Alkali, Babakalli

Almadni, Hani Mustafataher M

Ansell, Ray

Lak, Gyorgy Balint

English

The use of Lithium-ion secondary batteries in electric vehicles has now become a key component of high-performance energy storage systems. There are various factors that contribute significantly to the reduction in battery performance and subsequent damage of battery cells. The factors associated with battery degradation as a result of the charging and discharge rate is not new and has been investigated widely by many researchers. The maximum amount of electrical charge that a battery can store and deliver normally decreases over time. This capacity fade phenomenon is as a result of various degradation mechanisms within the battery. The degradation mechanisms are random in nature and in this paper, the degradation mechanisms associated with the change of battery cells electrical impedance, capacity and the life cycle are conducted. The 18650 battery cells is tested for 23 cycles on a trickle charge and the designated samples from each battery is subjected to different set temperatures range of (20°C-55 °C) at a constant time per experiment. Statistical analysis of the data is conducted using time series approach, and a reliability model is proposed in an attempt to predict the degradation or failure and hence improve the battery performance.</p

ResearchOnline@GCU

Time series analysis of high energy density lithium-ion batteries for electric vehiclesapplicationsAlmadni, Hani Mustafataher M; Alkali, Babakalli; Lak, Gyorgy Balint; Ansell, RayPublished in:Proceedings of the 16th International Conference on Condition Monitoring and Asset Management, BINDT 2019Publication date:2019Document VersionPublisher's PDF, also known as Version of recordLink to publication in ResearchOnlineCitation for published version (Harvard):Almadni, HMM, Alkali, B, Lak, GB & Ansell, R 2019, Time series analysis of high energy density lithium-ion batteries for electric vehicles applications. in Proceedings of the 16th International Conference on ConditionMonitoring and Asset Management, BINDT 2019. <https://www.bindt.org/events/CM2019/305-abstract/>General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please view our takedown policy at https://edshare.gcu.ac.uk/id/eprint/5179 for detailsof how to contact us.Download date: 24. Apr. 2022Time series analysis of high energy density lithium-ion batteries for electric vehicles applications   Hani Almadni , Dr.Babakalli Alkalli, Dr.Gyorgy Lak and Dr.Ray Ansell Glasgow, Scotland, G4 0BA, United Kingdom hani.almadni@gcu.ac.uk, babakalli.alkali@gcu.ac.uk, gyorgy.lak@gcu.ac.uk, rayansell51@outlook.com  Abstract  The use of Lithium-ion secondary batteries in electric vehicles has now become a key component of high-performance energy storage systems. There are various factors that contribute significantly to the reduction in battery performance and subsequent damage of battery cells. The factors associated with battery degradation as a result of the charging and discharge rate is not new and has been investigated widely by many researchers. The maximum amount of electrical charge that a battery can store and deliver normally decreases over time. This capacity fade phenomenon is as a result of various degradation mechanisms within the battery. The degradation mechanisms are random in nature and in this paper, the degradation mechanisms associated with the change of battery cells electrical impedance, capacity and the life cycle are conducted. The 18650 battery cells is tested for 23 cycles on a trickle charge and the designated samples from each battery is subjected to different set temperatures range of (20°C-55 °C) at a constant time per experiment. Statistical analysis of the data is conducted using time series approach, and a reliability model is proposed in an attempt to predict the degradation or failure and hence improve the battery performance. 1. Introduction  There are concerns surrounding the use of petroleum for vehicle propulsion, principally as a result of increased pollution, decreasing petroleum stock and environmnetal regulation. This has increased the awareness of the need to create alternative energy sources for powering vehicles. Electric vehicle or electric drive vehicle (EV) is a vehicle propelled by one or more electric motors, powered by entirely or partially by electro/chemical high energy which is stored in rechargeable battery packs(1). The most developed energy systems are based on lithium ion batteries which is provide an energy source for use in electric vehicles (2). Electric vehicle or electric drive vehicle (EV) is a vehicle propelled by one or more electric motors, powered by entirely or partially by electro/chemical high energy which is stored in rechargeable battery packs(3).  However, the battery pack is a set of any number of battery cells designed in a series, parallel or a combination of both to achieve the required voltage, capacity, or power density for electric vehicles. Likewise, rechargeable battery packs usually contain a Battery Management System (BMS) that regulate the vehicle's software and hardware operations such as voltage, current and temperature as well as balancing the cells during battery pack charge and discharge(4).The Lithium-ion cell is the fundamental unit of current commercial technologies that are used to produce and manufacture the electric  2 vehicles. The Lithium-Ion 18650 Battery Cells are cylindrical high volume cells that have been able to maintain a drop-in rechargeable cell of 3.7V having a capacity of at least 2600mAh as well as has the ability to handle close to 300 charge cycles (2).  The advantages of the Li-Ion battery include the fact that it is cost-effective, there is no requirement for priming when these batteries receive their first charge, low maintenance that ensures performance and the rate of self-discharge in Li-Ion batteries is much lower in comparison to other rechargeable battery sources.  These advantages and factors place the use of Li-Ion batteries as an excellent ideal and optimal choice in contrast to technologies such as nickel-metal-hydride and silver–zinc battery options for use in electric vehicles (5).  On the other hand the disadvantages of the Li-Ion battery is that they drop their charge over time (Self-discharge), suffer from ageing depending on the number of charge-discharge cycles and The performance of the battery cell drops drastically at low temperatures (2). The study presnted in this paper is based on the current developments of the electric vehicles in the technological sector that have enabled manufacturers to produce rechargeable electric vehicles powered by the energy stored in lithium-ion battery. The evaluation and investigation of the lithium-ion battery cell 18650 for 23 cycles on trickle charge (+95%) subjected to different set temperatures range of (20°C-55 °C) at a constant time per experiment. The equivalent circuit model is used within the framework of EIS software analyser. A forecasting model of the test data collected using the time series approach is conducted to determine the trend of the impedence and internal resistenace of the lithium-ion observations.  2. The the cell equivalent circuit  The equivalent circuit model of the internal impedance battery in EIS analyser software is shown in Figure 1;  Figure 1 The Battery Cell Equivalent circuit (ECM)  Algebraic expression of the model is given in equation (1); ! = R1 +	 1'()1 +	 1*2 + 11*3	 + 	'()2			      (1) Where: Z= Total internal impedance of the battery cell (Ω) R1 = Total internal resistance (Ω)  R2 = Charge transfer resistance (Ω) C1 = Charge transfer capacitance (F)   3 R3 = Solid electrolyte interphase resistance (Ω) C2 = Solid electrolyte interphase capacitance (F) j = imaginary part  ω = 2	"  f, ω is the angular frequency or angular speed (rad/s) f = frequency (Hz)  3. The experiment set-up   3.1 Expermental procedure   Figure 2 shows the schematic diagram of the main experiment considered in this paper. A cylindrical commercial version Lithium-Ion 18650 Battery Cells  (LIB) with a capacity of 2600 mAh is investigated in this study, and the LIB was subjected to electrochemical experiments at different controlled temperatures. The first measurements were performed at adjusted 25 C, then the battery was charging from manufacturer SoC 30 % to 100% and charging continually it from 95% to 100% as trickle charge for 23 cycles.     Figure 2 schematic diagram of the experiment main units  4 Arduino programme is used to set the required temperature and program the Versa stat to set up the LIB cycling. It was used also to monitor the experiment during the cycles. The experiments performed at different temperatures range (20, 25, 30, 40, 45, 55) for 23 cycles. The temperatures was adjusted using the Arduino controller by converting the temperature to the Volt. The charging experimentation was carried out using the Versa stat. For every 1 cycle run, the Versa stat measure and record the cell internal impedance.  The main goal in this paper is to use forecasting technique to conduct time series analysis in order to gain insight and have better control of the battery cell operation temperature, enhance the battery cell performance and increase the battery cell life cycle and state of health (SoH) and predict battery life. In order to achieve this we did the following;   • Measure the effect of the trickle charge on the Li-ion battery SOH. • investigation the impact of the ambient temperature on Li-ion battery SOH. • Assess the reliability of the Electrochemical Impedance Spectroscopy for Li-ion battery SOH at different battery temperatures. 3.2 The experiment set up:  The following steps is used for the experiment  1. place the battery cell in the aluminium cube in the right direction 2. Attached the clamp 3. Set up the Arduino software and send it to the microcontroller 4. Versa stat, select instrument active connection with the external experiment cell 5. save as (experiment new number) 6. check you have an active connection with right name on the top  7. click on run the experiment  8. approve data deletion  9. check the polarity (if wrong stop the experiment and repeat from step 1 and change the battery in the right direction) 10. Check the temperature during the experiment with the digital thermometer and calculate the average.  The experiment data is processed by the EIS Spectrum Analyser software program. EIS Spectrum Analyser is a standalone program for analysis and simulation of impedance spectra. Fitting the data processed is displayed on a graph and an example of the imported data from formatted text files is presented. The data displayed on the graphs of the EIS Spectrum Analyser shows the data before and after the data fitting process in Figure 3 and Figure 4 respectively.     5  Figure 3. Spectrum analyser data before   Figure 4. Spectrum analyser data after fitting process  Six experimental test run are conducted and the result in Table 1 summarise the temprature and the converted temperature to mV for all experiments.  During the experiment the some vital observation were made and the information recorded, the overall experiment event recorded include :  1. The system is overshooting for 110 Sec. 2. The system undershooting for 90 Sec. 3. The system takes 380 Sec. to be stable  4. Switch of the battery after each cycle to rest    6 Table 1. The summary of experiments set up in mV Sample Code T(°C)  T°C(mV) 01-T20 20°C 44.202 02-T25 25°C 55.252 03-T35 35°C 66.303 04-T40 40°C 88.404 05-T45 45°C 99.455 06-T55 55°C 121.555   4. The experimental results  The experiment result is summarised and presented in Table 2. Notice from the result the changing percent for each equivalent circuit components values for each battery cell after its experiment.  Table 2. The ECM values at after experiments  20°C 25°C 30°C 40°C 45°C 55°C R1 +1.868% + 1.41 % +0.019% + 0.78 % + 0.69 % + 0.178 % R2 - 5% -18.98% -24.98% - 40.98% +18% +32.42 C1 + 33.2% +47.91% + 34.45% + 38.88 -70.78% - 46.57 R3 +19.63% +14.09% -19.65% +1.04% +8.4% +23.70 C2 +83.44% +90.67% +83.84% +93.488% -31.81% - 23.52%   4.1 Forcasting   Quantitative forecasting model often look at time series. The time series is the seried of observation taken at regular inyterval. Is the case in the studet presented in this paper obervervations are made and data recorded at different temperature intervals. The main interest is the observe the patterns of the series of observation and to do that it is always useful to plot a graph to show the underlying pattern. A times series plot is fitted to the model observations at various temperature.   There are three most common patterns in time series and these are  • Constant series  • Series with trent  • Seasonal series    7  4.2 Time Series plot of (R1) Total Internal Ohmic Resistance of Li-ion Battery Cell   The graph in Figure 5 display the different of the total internal Ohmic Resistance for each Li-ion Battery Cell at different maintaining temperatures during the test period. Notice on the graph, temperature 25°C shows a significant change in resistance over the experiment period compared to the other cell operating temperatures.   Figure 5.  The effect of the different temperatures over the experiment period The cell internal ohmic resistance at temperature 30 °C has an unusual blip at time 3947.6 seconds, this could be a result of electrical change during the test or software error.  Table 4 give a summary of the range of the Total Internal Ohmic Resistance R1 of Li-ion Battery Cell at Various Temperature tests:  Table 4 Time series analysis of R1 at different temperatures  Minimum Maximum R1-T20 989.22 1067.10 R1-T25 934.31 1104.70 R1-30 938.03 990.75 R1-T40 808.140 821.540 R1-T45 842.750 882.970 R1-T55 840.500 880.480  8 The experimental data for each operating temperature shows the effect over the test. The minimum change of R1 is during T30°C over the test period indicate good battery performance. The maximum change of R1 is T20° over the test period, and this indicate low battery performance.  4.3 Time Series Plot of (R2) charge transfer resistance (Ω) of Li-ion Battery Cell   The graph in Figure 6 display the difference of charge transfer resistance for each Li-ion Battery Cell at different maintaining temperatures during the test period. The charge transfer resistance reducees as battery temperatures increases. This is an intresting observation compared to the internal ohmics resisitance.    Figure 6. Effect of Deferent Temperature on R2 over experiment period  The next result in Table 5 is a summary of the range of the charge transfer resistance R2 of Li-ion Battery Cell at Various Temperature tests.  The experimental data for each operating temperature shows the effect over the test. In this case the minimum change of R2 takes place at T30°C over the test period, this indicate good battery performance. The maximum change of R1 is T20°C over the test period, indicate low battery performance.     9 Table 5 Time series analysis of R2 at deferent temperature  Minimum Maximum R2-T20 92.171 116.440 R2-T25 92.303 113.940 R2-T30 76.591 102.110 R2-T40 30.436 51.575 R2-T45 15.713 27.234 R2-T55 18.791 34.489  4.4 Time Series Plot of (C1) Charge transfer capacitance (F) of Li-ion Battery   Figure 7 displayed the difference of Charge transfer capacitance for each Li-ion Battery Cell at different maintaining temperatures during the test period. The Charge transfer capacitance increases as battery temperatures increases.   Figure 7.  Effect of Deferent Temperature on C1 over experiment period  Table 6 summarise the range of the Charge transfer capacitance C1 of Li-ion Battery Cell at Various Temperature tests:   10 Table 6.  Summary of C1 changing over the experimental period  Minimum Maximum C1-T20 0.00001790 0.00002680 C1-T25 0.00001500 0.00002880 C1-T30 0.00002140 0.00003260 C1-T40 0.00005030 0.00008230 C1-T45 0.00005800 0.00019854 C1-T55 0.00006840 0.00012802  4.5 Time Series Plot of (C2) Solid electrolyte interphase capacitance (F) of Li-ion Battery   The graph in Figure 8 display the different of Solid electrolyte interphase capacitance for each Li-ion Battery Cell at different maintaining temperatures during the test period. The Solid electrolyte interphase capacitance increases as battery temperatures increases.   Figure 8.  Effect of temperatures on C2 over experimental period Table 7 summarise the range of the Solid electrolyte interphase capacitance C2 of Li-ion Battery Cell at Various Temperature tests:   11 Table 7.  Summary of the range C2 of Li-ion Battery Cell at Various Temperatures  Minimum Maximum C2-T20 0.0001863 0.0011255 C2-T25 0.0001035 0.0011103 C2-T30 0.0004026 0.0024924 C2-T-40 0.000505 0.007558 C2-T45 0.000505 0.010244 C2-T55 0.002798 0.011608  4.6 Time Series Plot of (R3) Solid electrolyte interphase resistance (Ω) of Li-ion   Figure 9 displayed the different of Solid electrolyte interphase resistance for each Li-ion Battery Cell at different maintaining temperatures during the test period. The Solid electrolyte interphase resistance (Ω) decreases as battery temperatures increases.   Figure 9 Effect of Deferent Temperature on R3 over experiment period Table 8 give a summary of the range of the Solid electrolyte interphase resistance R3 of Li-ion Battery Cell at Various Temperature tests:   12 Table 8 Time series analysis of R3 at deferent temperature  Minimum Maximum R3-T20 27.319 49.914 R3-T25 25.88 65.07 R3-T30 21.637 28.680 R3T40 11.469 17.727 R3-T45 3.535 25.394 R3T55 12.9800 17.0140   5. Conclusions  The times series analysis is conducted on the data collected from the experiment test run in this study. Graphical display of results presented show differences in the patterns of the underlying temperatures of the internal impedence of the battery cell. These differences appear to be a random noise which is superimposed by yhe underlying patterns as shown in all the time series graphs presented in the paper. Because of the noise, there is almost always an errors in the forecast, and if we measure the error, we will thereby have a way of measuring the the accuracy of the forcast and minimizing the errors of the data. The results presented give a very good insight about the underlying impedence at different temperature range. The research and data analysis from this experiment is still on going and we intend to present further result in future publications.   Acknowledgements  This research was supported by The Ministry of Higher Education of Saudi Arabia government represented by Royal Embassy of Saudi Arabia Cultural Bureau in London.            13  References  1.   Un-Noor, Fuad & Sanjeevikumar, P & MIHET-POPA, Lucian & Nurunnabi Mollah, Mohammad & Hossain, Eklas. (2017). A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development. Energies. 10. 10.3390/en10081217.  2. 18650 Battery | BATTERY BRO. (2017). 18650 Battery | BATTERY BRO. [online] Available at: https://batterybro.com [Accessed 7 March. 2019].  3. K.T. Chau, 21 - Pure electric vehicles,Editor(s): Richard Folkson,Alternative Fuels and Advanced Vehicle Technologies for Improved Environmental Performance,Woodhead Publishing,2014,Pages 655-684,  4. Languang Lu, Xuebing Han, Jianqiu Li, Jianfeng Hua, Minggao Ouyang,A review on the key issues for lithium-ion battery management in electric vehicles,Journal of Power Sources,Volume 226,2013,Pages 272-288,ISSN 0378-7753.  5.  electronics-notes (2019) [online] Avalible at : https://www.electronics notes.com/articles/electronic_components/battery-technology/li-ion-lithium-ion-advantages-disadvantages.php [Accessed 10 March. 2019].		

Time series analysis of high energy density lithium-ion batteries for electric vehicles applications

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Time series analysis of high energy density lithium-ion batteries for electric vehicles applications

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