Lithium-ion Battery Discharge Research Articles

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport. Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes.Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge.To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). These are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries.We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2%) in forecasting the discharge behavior of new unseen cathodes.

Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model

Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility.

Investigation and Optimization of Fast Cold Start of 18650 Lithium-Ion Cell by Heating Film-Based Heating Method

In this paper, based on the multi-scale multi-domain (MSMD) battery modeling approach, the NTGK model was used to model the 18650 cylindrical lithium-ion single battery on the electrochemical sub-scale. The model was successful, as it was able to fit the experimental voltage and temperature of the battery at different temperatures. Lithium-ion battery discharge capacity and energy output can be improved during cold starting by preheating and insulation, as demonstrated by a comparison of the impacts of heat transfer coefficient and preheating duration at −20 °C ambient temperature. For the traditional heating method, the heating model of heating film (HF) and liquid-cooled plate (LCP) is constructed in this paper, and the heating performance of both is compared by Fluent. Analysis of the energy balance of Li-ion battery at low temperatures has been presented, showing that Li-ion battery requires a suitable start-up temperature to maximize energy output. Taking care of the problem of excessive temperature difference inside the battery due to excessive heating power, we investigated the effects of axial thermal conductivity, heating power, and heating area on the heating uniformity of the battery in this paper. Finally, a multi-stage stepped power (MSP) heating method was proposed to improve the temperature control accuracy of HF. A level orthogonal test L16(43) without interaction was designed to determine the degree of influence of each parameter on the temperature control performance and the optimal level combination, revealing that the optimized maximum temperature and temperature control rate were reduced by 4.09% and 40.53%, respectively, when compared to direct heating.

A Novel Evaluation Criterion for the Rapid Estimation of the Overcharge and Deep Discharge of Lithium-Ion Batteries Using Differential Capacity

Differential capacity dQ/dU (capacitance) can be used for the instant diagnosis of battery performance in common constant current applications. A novel criterion allows state-of-charge (SOC) and state-of-health (SOH) monitoring of lithium-ion batteries during cycling. Peak values indicate impeding overcharge or deep discharge, while dSOC/dU = dU/dSOC = 1 is close to “full charge” or “empty” and can be used as a marker for SOC = 1 (and SOC = 0) at the instantaneous SOH of the aging battery. Instructions for simple state-of-charge control and fault diagnosis are given.

Prediction of Li-Ion Battery Discharge Patterns in IoT Devices Under Random Use Via Machine Learning Algorithms

Abstract This study presents foreseeing of the Lithium-ion battery discharge models for the Internet of Things (IoT) devices under randomized use patterns. IoT systems run in harmony with the human–machine interface, communication protocols and sharing data so long as uninterrupted data communication is exploited for their devices. Hence, forecasting the battery discharge duration is a very important issue for the regularization of IoT device performances. The well-known discharge duration is generally about the age-related electrochemical phenomena of an electrochemistry for Lithium-ion battery. The discharge changes of the battery were obtained from the input–output dynamics of the random battery use obtained from the randomized battery usage dataset in the NASA Ames prognostics data repository. They were investigated by machine learning methods and their results were estimated for life expectancy regularization of the IoT devices. In order to find the appropriate models of battery usage under randomized patterns, artificial neural network (ANN), Gaussian process and nonlinear regression models are evaluated in terms of battery capacity and internal resistance change as a function of discharged energy. The ${R}^2$, Adjusted ${R}^2$, root-mean-square-error (RMSE) and normalized-mean-square-error (NMSE) criteria were used to compare the performances of the obtained models for different settings. According to the results, ANN model, with settings of radial basis function activation function within single hidden-layer, and 20 hidden-layer neurons, shows the best performance in terms of ${R}^2=1.0000$ and $\mathrm{NMSE}={\mathrm{1.7384.10}}^{-4}$ metrics. $\mathrm{RMSE}={\mathrm{0.9896.10}}^{-4}$ is achieved by the ANN model with the settings of single hidden-layer with 10 neurons and hyperbolic-tangent activation function.

Discharge of lithium-ion batteries in salt solutions for safer storage, transport, and resource recovery

The use of lithium-ion batteries (LIBs) has grown in recent years, making them a promising source of secondary raw materials due to their rich composition of valuable materials, such as Cobalt and Nickel. Recycling LIBs can help reduce fossil energy consumption, CO2 emissions, environmental pollution, and consumption of valuable materials with limited supplies. On the other hand, the hazards associated with spent LIBs recycling are mainly due to fires and explosions caused by unwanted short-circuiting. The high voltage and reactive components of end-of-life LIBs pose safety hazards during mechanical processing and crushing stages, as well as during storage and transportation. Electrochemical discharge using salt solutions is a simple, quick, and inexpensive way to eliminate such hazards. In this paper, three different salts (NaCl, Na2S, and MgSO4) from 12% to 20% concentration are investigated as possible candidates. The effectiveness of discharge was shown to be a function of molarity rather than ionic strength of the solution. Experiments also showed that the use of ultrasonic waves can dramatically improve the discharge process and reduce the required time more than 10-fold. This means that the drainage time was reduced from nearly 1 day to under 100 minutes. Finally, a practical setup in which the tips of the batteries are directly immersed inside the salt solution is proposed. This creative configuration can fully discharge the batteries in less than 5 minutes. Due to the fast discharge rates in this configuration, sedimentation and corrosion are also almost entirely avoided.

Studies on the deposition of copper in lithium-ion batteries during the deep discharge process

End-of-life lithium-ion batteries represent an important secondary raw material source for nickel, cobalt, manganese and lithium compounds in order to obtain starting materials for the production of new cathode material. Each process step in recycling must be performed in such a way contamination products on the cathode material are avoided or reduced. This paper is dedicated to the first step of each recycling process, the deep discharge of lithium-ion batteries, as a prerequisite for the safe opening and disassembling. If pouch cells with different states of charge are connected in series and deep-discharged together, copper deposition occurs preferably in the cell with the lower charge capacity. The current forced through the cell with a low charge capacity leads, after lithium depletion in the anode and the collapse of the solid-electrolyte-interphase (SEI) to a polarity reversal in which the copper collector of the anode is dissolved and copper is deposited on the cathode surface. Based on measurements of the temperature, voltage drop and copper concentration in the electrolyte at the cell with the originally lower charge capacity, the point of dissolution and incipient deposition of copper could be identified and a model of the processes during deep discharge could be developed.

Thick Sintered Electrode Lithium-Ion Battery Discharge Simulations: Incorporating Lithiation-Dependent Electronic Conductivity and Lithiation Gradient Due to Charge Cycle

In efforts to increase the energy density of lithium-ion batteries, researchers have attempted to both increase the thickness of battery electrodes and increase the relative fractions of active material. One system that has both of these attributes are sintered thick electrodes comprised of only active material. Such electrodes have high areal capacities, however, detailed understanding is needed of their transport properties, both electronic and ionic, to better quantify their limitations to cycling at higher current densities. In this report, efforts to improve models of the electrochemical cycling of sintered electrodes are described, in particular incorporation of matrix electronic conductivity which is dependent on the extent of lithiation of the active material and accounting for initial gradients in lithiation of active material in the electrode that develop as a consequence of transport limitations during charging cycles. Adding in these additional considerations to a model of sintered electrode discharge resulted in improved matching of experimental cell measurements.

A power transfer model-based method for lithium-ion battery discharge time prediction of electric rotatory-wing UAV

This paper develops a power transfer model-based method to estimate real-time state of energy (SOE) and predict end of discharge (EOD) time of rotatory-wing UAVs lithium batteries under dynamic operational conditions. A discrete-time state-space model of battery is first established to model the process of battery power consumption and establish a mapping of battery unobservable state of energy (SOE) to measurable parameters such as voltage and current. Then a power consumption model of UAV is established based on predetermined flight mission of UAV using aerodynamics and momentum theory, which estimates power consumption of UAV under different operational conditions. Its calculation results can be directly used in battery state-space model as its input, while its model parameters are simultaneously updated by online measurements of consumed power. Finally, a Particle Filter (PF) approach with Adam optimization algorithm is developed to estimate SOE and predict EOD time on-line, and better prediction results compared to conventional PF are achieved. Real experiments on UAV verify the effectiveness of the proposed method.

Hazardous characteristics of charge and discharge of lithium-ion batteries under adiabatic environment and hot environment

The 18,650 lithium-ion battery was charged and discharged at different rates, first in an environment with heat source, and then in one with heat-insulation, to study the effects of different thermal environments on the thermal behavior of lithium batteries. In the case of charging and discharging in adiabatic environments, the battery did not have thermal runaway, and the maximum temperature and temperature rise rate increased with the increase of the charging and discharging rates. During the 4 C discharge process, the resistance increased due to the action of the Positive Temperature Coefficient (PTC) element, and the loop current decreased, which made the highest temperature lower than that in the 3 C discharge process. In the presence of an external heat source, when charged and discharged at different rates, the battery was thermally out of control. The thermal runaway behavior of the battery during charging was a large amount of gas leakage. At the time of discharge, due to the high initial State of Charge (SOC), at the 0.5 C, 1 C, 2 C rates, there was thermal runaway following a large amount of gas leakage. The same phenomenon also occurred at the 3 C, 4 C rates, but without continuous combustion. In the environment of external heat source, results showed that the thermal runaway heat release was always greater in the charge process than in the discharge process. With the increase of the charge and discharge currents, the battery power before and after the thermal runaway remained almost the same. Therefore, the ratio of the thermal runaway heat release in the charge process to that in the discharge process kept increasing and finally approached 1.

Challenging the concept of electrochemical discharge using salt solutions for lithium-ion batteries recycling

The use of lithium-ion batteries (LIB) has grown significantly in recent years, making them a promising source of secondary raw materials due to their rich composition of valuable materials such as Co, Ni and Al. However, the high voltage and reactive components of LIBs pose safety hazards during crushing stages in recycling processes, and during storage and transportation. Electrochemical discharge by immersion of spent batteries in salt solutions has been generally accepted as a robust and straightforward discharging step to address these potential hazards. Nonetheless, there is no clear evidence in the literature to support the use of electrochemical discharge in real systems, neither are there clear indications of the real-world limitations of this practice. To that aim, this work presents a series of experiments systematically conducted to study the behavior of LIBs during electrochemical discharge in salt solutions. In the first part of this study, a LIB sample was discharged ex-situ using Pt wires connected to the battery poles and submerged into the electrolyte solution on the opposite end. The evolution of voltage in the battery was measured for solutions of NaCl, NaSO4, FeSO4, and ZnSO4. The results indicate that, among the electrolytes used in the present study, NaCl solution is the most effective for LIBs discharge. The discharge of LIB using sulfate salts is however only possible with the aid of stirring, as deposition of solid precipitated on the electrodes hinder the electrochemical discharge. Furthermore, it was found that the addition of particulates of Fe or Zn as sacrificial metal further enhances the discharging rate, likely due to an increased contact area with the electrolyte solution. While these findings support the idea of using electrochemical discharge as a pre-treatment of LIBs, severe corrosion of the battery poles was observed upon direct immersion of batteries into electrolyte solutions. Prevention of such corrosion requires further research efforts, perhaps focused on a new design-for-recycling approach of LIBs.

Effect of Dimethallyl Carbonate Addition on Thermal Stability of Lithium Ion Batteries

The role of dimethallyl carbonate (DMAC), which has a multiple functional group, as an additive in electrolyte have been studied for improving thermal stability of lithium ion batteries (LIB). During the initial charging of the negative electrode with an electrolyte that contains the DMAC additive, film formation occurs near 1 V vs. Li/Li+. Furthermore, increases in the amount of DMAC additive are accompanied by increases in the height of the dq/dV peaks and film formation progresses. The time-of-flight secondary ion mass spectrometry (TOF-SIMS) results show that increasing the amount of DMAC additive promotes the formation of aliphatic hydrocarbons of C5 or more in the Solid-Electrolyte-Interfase (SEI) and polymerization proceeds. The temperature programmed desorption mass spectrometry (TPD-MS) results show that the DMAC additive raises the peak temperature for CO2 generation to 100°C or higher. The DMAC additive suppresses self discharge of LIB and suppresses the increase in direct current resistance (DCR) after storage at 50°C.

Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery’s State of Charge (SOC) and State of Life (SOL) by utilizing the battery’s physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations.

Space matters: Li+ conduction versus strain effect at FePO4/LiFePO4 interface

FePO4/LiFePO4 (FP/LFP) interfacial strain, giving rise to substantial variation in interfacial energy and lattice volume, is inevitable in the (de)lithiation process of LiFePO4, a prototype of Li ion battery cathodes. Extensive theoretical and experimental research has been focused on the effect of lattice strain energy on FP/LFP interface propagation orientation and cyclic stability of the electrode. However, the essential effect of strain induced lattice distortion on Li+ transport at the FP/LFP interface is typically overlooked. In this report, a coherent interface model is derived to evaluate quantitatively the correlation between FP/LFP lattice distortion and Li+ conduction. The results illustrate that the effect of lattice strain on Li+ conduction depends strongly on FP/LFP interface orientations. Lattice strain induces a 90% decrease of Li+ conductivity in ac-plane oriented (de)lithiation at room temperature. The opposite effect of lattice strain on delithiation and lithiation for ab- and bc-orientations is elucidated. In addition, the effect of lattice strain tends to be more pronounced at a lower working temperature. This study provides an efficient platform to comprehend and manipulate Li+ conduction in the charge and discharge of lithium ion batteries, the large-scale application of which is frequently challenged by limited in-cell ion conduction.

Length-Scale-Dependent Phase Transformation of LiFePO4 : An In situ and Operando Study Using Micro-Raman Spectroscopy and XRD.

The charge and discharge of lithium ion batteries are often accompanied by electrochemically driven phase-transformation processes. In this work, two in situ and operando methods, that is, micro-Raman spectroscopy and X-ray diffraction (XRD), have been combined to study the phase-transformation process in LiFePO4 at two distinct length scales, namely, particle-level scale (∼1 μm) and macroscopic scale (∼several cm). In situ Raman studies revealed a discrete mode of phase transformation at the particle level. Besides, the preferred electrochemical transport network, particularly the carbon content, was found to govern the sequence of phase transformation among particles. In contrast, at the macroscopic level, studies conducted at four different discharge rates showed a continuous but delayed phase transformation. These findings uncovered the intricate phase transformation in LiFePO4 and potentially offer valuable insights into optimizing the length-scale-dependent properties of battery materials.

Electrically conductive silk fibroin/glycerine/polypyroll biofilms for biomedical applications

The use of lithium-ion batteries (LIB) has grown significantly in recent years, making them a promising source of secondary raw materials due to their rich composition of valuable materials such as Co, Ni and Al. However, the high voltage and reactive components of LIBs pose safety hazards during crushing stages in recycling processes, and during storage and transportation. Electrochemical discharge by immersion of spent batteries in salt solutions has been generally accepted as a robust and straightforward discharging step to address these potential hazards. Nonetheless, there is no clear evidence in the literature to support the use of electrochemical discharge in real systems, neither are there clear indications of the real-world limitations of this practice. To that aim, this work presents a series of experiments systematically conducted to study the behavior of LIBs during electrochemical discharge in salt solutions. In the first part of this study, a LIB sample was discharged ex-situ using Pt wires connected to the battery poles and submerged into the electrolyte solution on the opposite end. The evolution of voltage in the battery was measured for solutions of NaCl, NaSO4, FeSO4, and ZnSO4. The results indicate that, among the electrolytes used in the present study, NaCl solution is the most effective for LIBs discharge. The discharge of LIB using sulfate salts is however only possible with the aid of stirring, as deposition of solid precipitated on the electrodes hinder the electrochemical discharge. Furthermore, it was found that the addition of particulates of Fe or Zn as sacrificial metal further enhances the discharging rate, likely due to an increased contact area with the electrolyte solution. While these findings support the idea of using electrochemical discharge as a pre-treatment of LIBs, severe corrosion of the battery poles was observed upon direct immersion of batteries into electrolyte solutions. Prevention of such corrosion requires further research efforts, perhaps focused on a new design-for-recycling approach of LIBs.

Ab Initio-Based Multiscale Simulations of Conversion Reactions in Lithium-Ion Batteries

The development of theoretical methods to correlate the chemical and structural properties of electrode materials with their electrochemical behavior in energy storage devices is of crucial importance for consistent interpretation of experimental data and for their optimization toward end-user applications [1-3].In this work a new multiscale model is presented allowing to describe solid phase formation and separation during discharge-charge in Lithium Ion Battery (LIB) cathodes using conversion materials such as CoO, CuO or CoP [4-5]. The adopted theoretical framework extends a previous approach extensively developed for the multiscale modeling of fuel cells [6-7]. The model is supported on a Cahn-Hilliard-based phase field approach parametrized with ab initio-extracted parameters such as the interphase (solid/solid, solid/liquid) energies and binary (solid/solid phase) diffusion coefficients [8-9]. The electrochemical conversion reaction MO + Li+ + e- ↔ Li2O + M° and associated Li+ and e- transports are locally resolved across the multiphase MO/Li2O/M° system. The integration of this multiscale phase field model into a cell level model accounting for electrolyte/active material interfaces (in house developed simulation package "MS LIBER-T" [10]) allows simultaneously simulating the microstructural evolution of the multiphase active particles (Figure) and the associated potential vs. capacity characteristics during the LIB discharge and charge. Simulation results provide good agreement with experimental findings, in particular for the evolution of the active particles during discharge from microstructured to nanostructured morphologies [4-5,11].