Mapping internal temperatures during high-rate battery applications
Thomas M. M. HeenanIsabella MombriniAlice V. LlewellynStefano ChecchiaChun TanMichael J. JohnsonAnmol JnawaliGastón GarbarinoRhodri JervisDan J. L. BrettMarco Di MichielPaul R. Shearing
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The State of Health (SOH) of the battery is often represented either using the decrease in the capacity of the battery or the increase in the internal resistance of the battery. While these indices are commonly used, they do not provide any insight on the reasons for the degradation of the health of the battery. Understanding battery aging and the impact it has on the working/performance of the battery is required to determine the State of Function (SOF) of the battery for that particular application. The SOF of the battery can provide information on the current applicability of the battery to the application. The Remaining Useful Life (RUL) of the battery is also highly dependent on the current and past operating conditions. Determining the reason behind the degradation and the impact on the health can also help determine the RUL or provide feedback to the user on alternate usage patterns to prolong the RUL. This paper uses a first principle based degradation model to determine the sensitivity of the terminal voltage and capacity of the battery to the degradation of the concentration of lithium ions in the anode/negative electrode.
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The operating temperature has a significant impact on the performance of electrochemical systems such as batteries. The amount of energy stored inside depends largely on the temperature (especially under 0°C). To maintain a good energy performance of the electric vehicle, it is necessary to know the parameters that characterize the battery to allow a better approximation of the amount of remaining energy in the battery. This document presents an approximated battery model and shows the effect of the low temperature on the parameters of the battery. The objective of this document is to show a simple method to identify the internal resistance and the specific heat of the battery by measuring the open circuit voltage through time, this will allow to know how to manage the energy of the battery that is required to perform the cold start of an electric vehicle.
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This paper describes a mismatch of internal resistance battery LiFePO 4 when assembling into battery pack. Internal resistance battery is a number that states the value of resistance that exist within the battery component, it will affect the State of Health, State of Charge, life time, until the heat generated by the battery. The tools used in this experiment include the internal resistance tester EQ-MSK-BK300, battery analyzer BST8-3, and the results are observed in real time on software TC5.3. The experimental results showed that there are differences produced by some of the battery pack that has a different internal resistance.
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A new way to determine the optimal remanufacturing point of lithium ion batteries for electric vehicle has been proposed in this paper. The proposed optimal remanufacturing point can avoid the sharp degradation stage of battery and ensure most of active materials covered in electrodes reused in remanufacturing process with modest cost and energy. In order to find out the optimal remanufacturing point in our experiment, the charge-discharge cycle of lithium ion batteries was carried out by using battery testing system and internal resistance meter, which can obtain enough data such as the degradation of capacity, the change of internal resistance, the charging and discharging rate and the cycle life of battery for analysis. Then the optimal battery's remanufacturing point about 500-550 cycle times has been found.
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Accurately predicting the lifetime of lithium-ion batteries in the early stage is critical for faster battery production, tuning the production line, and predictive maintenance of energy storage systems and battery-powered devices. Diverse usage patterns, variability in the devices housing the batteries, and diversity in their operating conditions pose significant challenges for this task. The contributions of this paper are three-fold. First, a public dataset is used to characterize the behavior of battery internal resistance. Internal resistance has non-linear dynamics as the battery ages, making it an excellent candidate for reliable battery health prediction during early cycles. Second, using these findings, battery health prediction models for different operating conditions are developed. The best models are more than 95% accurate in predicting battery health using the internal resistance dynamics of 100 cycles at room temperature. Thirdly, instantaneous voltage drops due to multiple pulse discharge loads are shown to be capable of characterizing battery heterogeneity in as few as five cycles. The results pave the way toward improved battery models and better efficiency within the production and use of lithium-ion batteries.
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A novel method to monitor dynamically the internal pressure of sealed battery with no damage of the battery by using of resistance strain measurement technique is presented. This technique employs the resistence strain gauges in touch of the battery case and to detect the minimal change in the battery deformation due to the increasing internal pressure, electrode expantion or intrenal temperature rise. Application of the technique for the study of the effects of the battery processing factors on the battery performance is described taking Ni MH battery as example.
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With widespread applications for lithium-ion batteries in energy storage systems, the performance degradation of the battery attracts more and more attention. Understanding the battery’s long-term aging characteristics is essential for the extension of the service lifetime of the battery and the safe operation of the system. In this paper, lithium iron phosphate (LiFePO4) batteries were subjected to long-term (i.e., 27–43 months) calendar aging under consideration of three stress factors (i.e., time, temperature and state-of-charge (SOC) level) impact. By means of capacity measurements and resistance calculation, the battery’s long-term degradation behaviors were tracked over time. Battery aging models were established by a simple but accurate two-step nonlinear regression approach. Based on the established model, the effect of the aging temperature and SOC level on the long-term capacity fade and internal resistance increase of the battery is analyzed. Furthermore, the storage life of the battery with respect to different stress factors is predicted. The analysis results can hopefully provide suggestions for optimizing the storage condition, thereby prolonging the lifetime of batteries.
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