Search Results (16)

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries confronts two critical challenges: the extreme scarcity of labeled data in large-scale operational datasets and the mismatch between existing methods (relying on full charging–discharging conditions) and shallow charging–discharging conditions prevalent in real-world scenarios. To address these challenges, this study proposes a self-supervised learning framework for SOH estimation. The framework employs a dual-time-scale collaborative pre-training approach via masked voltage sequence reconstruction and interval capacity prediction tasks, enabling automatic extraction of cross-time-scale aging features from unlabeled data. Innovatively, it integrates domain knowledge into the attention mechanism and incorporates time-varying factors into positional encoding, significantly enhancing the capability to extract battery aging features. The proposed method is validated on two datasets. For the standard dataset, using only 10% labeled data, it achieves an average RMSE of 0.491% for NCA battery estimation and 0.804% for transfer estimation between NCA and NCM. For the shallow-cycle dataset, it achieves an average RMSE of 1.300% with only 2% labeled data. By synergistically leveraging massive unlabeled data and extremely sparse labeled samples (2–10% labeling rate), this framework reduces the labeling burden for battery health monitoring by 90–98%, offering an industrial-grade solution with near-zero labeling dependency. Full article

Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount of spent batteries has, therefore, become significant. Whether recycled or repurposed for second-life applications, knowing their chemistry type can lead to higher efficiency. In this paper, we propose a novel machine learning-based approach for accurate chemistry identification of the electrode materials in LIBs based on their temperature dynamics under constant current cycling using gated recurrent unit (GRU) networks. Three different chemistry types, namely lithium nickel cobalt aluminium oxide cathode with silicon-doped graphite anode (NCA-GS), nickel cobalt aluminium oxide cathode with graphite anode (NCA-G), and lithium nickel manganese cobalt oxide cathode with graphite anode (NMC-G), were examined under four conditions, 0.2 C charge, 0.2 C discharge, 1 C charge, and 1 C discharge. Experimental results showed that the unique characteristics in the surface temperature measurement during the full charge or discharge of the different chemistry types can accurately carry out the classification task in both experimental setups, where the model is trained on data under different cycling conditions separately and jointly. Furthermore, experimental results show that the proposed approach for chemistry type identification based on temperature dynamics appears to be more universal than voltage characteristics. As the proposed approach has proven to be efficient in the chemistry identification of the electrode materials LIBs in most cases, we believe it can greatly benefit the recycling and second-life application of spent LIBs in real-life applications. Full article

Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB’s health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 °C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing. Full article

Recycling processes of lithium-ion batteries used in electric and hybrid vehicles are widely studied today. To perform such recycling routes, it is necessary to know the composition of these batteries and their components. In this work, three pouch and three cylindrical LIBs were discharged, dismantled, and characterized, having their compositions known and quantified. The dismantling was performed using scissors, pliers, and a precision cutter equipment. The organic liquid electrolyte was quantified via mass loss after it evaporated at 60 °C for 24 h. The separators were analyzed using Fourier-transform infrared spectroscopy (FTIR), and the cathode and anode active materials were analyzed using a scanning electronic microscope coupled to an energy-dispersive spectroscope (SEM-EDS), X-ray diffraction (XDR), and energy-dispersive X-ray fluorescence spectrometry (EDXRF). All LIBs were identified by type (NCA, NMC 442, NMC 811, LCO, and two LFP batteries), and a preliminary economic evaluation was conducted to understand their potential economic value (in USD/t). Both results (characterization and preliminary economic evaluation) were considered to discuss the perspective of recycling towards a circular economy for end-of-life LIBs. Full article

Recycling lithium-ion batteries provides sustainable raw materials. Crushing and separation are necessary for extracting metals, like lithium, from batteries. Crushing a battery carries a risk of fire or explosion. Fully discharging the battery is crucial for safe production. Discharging batteries in a salt solution is a simple and cost-effective large-scale process. However, it is important to note that there is a potential risk of corrosion and loss of battery elements when batteries are immersed in a salt solution. The purpose of this study is to investigate the effectiveness of two distinct methodologies at enhancing the voltage drop of a cylindrical battery when immersed in a salt solution while preventing corrosion. These techniques involve the application of iron and copper accelerators. A 20 wt.% salt water solution was chosen based on the research of several researchers. As the current flows through the metal parts, it encounters electrical resistance and forms an electric circuit with the electrolyte solution. This interaction converts electrical energy into various physical–electrical–electrochemical phenomena, leading to a decrease in battery voltage. Research revealed that the battery can be discharged up to 100% within 4 h without causing corrosion to its components. Another point to note is that if copper conductors are used, it is possible to decrease the battery voltage by around 90% within 8 h. The gap between the copper conductor and the battery had a direct impact on the battery’s discharge rate. Reducing the distance significantly increased the discharge rate, as confirmed by experimental evidence. This discharge mechanism was thoroughly described in a schematic, and, to further explain the electrochemical reaction, the Pourbaix diagram was utilized for both the Fe-Na-Cl and Cu-Na-Cl systems. Moreover, our theoretical predictions were validated through a chemical and mineralogical analysis of the precipitates that formed in the solution. Full article

Lithium nickel–cobalt–aluminum oxide (NCA) is a promising cathode material for lithium-ion batteries due to its high energy density of more than 274 mAh/g. However, thermal runaway inhibits its practical applications. Lithium ferromanganese phosphate (LFMP), due to its olivine structure, can effectively stabilize the surface stability of NCA and reduce the exothermic reactions that occur during thermal runaway. LFMP can also inhibit cathode expansion and contraction during charging and discharging. To improve the conductivity of an NCM–LFMP composite electrode, three different conductive additives, namely carbon black, carbon nanotubes (CNTs), and graphene, were introduced into the electrode. Finally, battery safety tests were conducted on 1.1 Ah pouch cells fabricated in the present study. The energy density of the NCA–LFMP 1.1 Ah lithium-ion pouch cells with only 0.16% CNT content reached 224.8 Wh/kg. The CNT–NCA–LFMP pouch cell was also the safest among the cells tested. These results provide a strategy for designing high-energy-density and safe pouch cells for energy storage device applications. Full article

As lithium-ion battery (LIB) cells degrade over time and usage, it is crucial to understand their remaining capacity, also known as State of Health (SoH), and inconsistencies between cells in a pack, also known as cell-to-cell variation (CtCV), to appropriately operate and maintain LIB packs. This study outlines efforts to model pack SoH and SoH CtCV of nickel-cobalt-aluminum (NCA) and lithium-iron-phosphate (LFP) battery packs consisting of four cells in series using pack-level voltage data. Using small training data sets and rapid testing procedures, partial least squares regression (PLS) models were built and achieved a mean absolute error of 0.38% and 1.43% pack SoH for the NCA and LFP packs, respectively. PLS models were also built that correctly categorized the packs as having low, medium, and high-ranked SoH CtCV 72.5% and 65% of the time for the NCA and LFP packs, respectively. This study further investigates the relationships between pack SoH, SoH CtCV, and the voltage response of the NCA and LFP packs. The slope of the discharge voltage response of the NCA packs was shown to have a strong correlation with pack dynamics and pack SoH, and the lowest SoH cell within the NCA packs was shown to dominate the dynamic response of the entire pack. Full article

Lithium-ion (Li-ion) batteries with Ni-based cathodes are leading storage technology in the fields of electric vehicles and power-grid applications. NCA (LiNiCoAlO₂) batteries are known for their troublesome degradation tendencies, and this susceptibility to degradation raises questions regarding the safety of their usage. Hence, it is of vital importance to analyse the degradation of NCA batteries via methods which are applicable to onboard systems, so that the changes in the battery’s state of health can be addressed accordingly. For this purpose, it is crucial to study batteries stressed by various conditions which might induce degradation of different origins or magnitudes. Methods such as electrochemical impedance spectroscopy (EIS), galvanostatic intermittent titration technique (GITT), and incremental capacity analysis (ICA) have been used in battery research for years, however, there is a lack of published studies which would analyse the degradation of NCA batteries by simultaneous usage of these methods, which is essential for a comprehensive and confirmatory understanding of battery degradation. This study intends to fill this research gap by analysing the degradation of NCA batteries via simultaneous usage of EIS, GITT, and ICA methods for common stress-inducing operating conditions (over-charge, over-discharge, and high-current charging). Full article

Lithium-ion batteries (LIBs) are employed when high energy and power density are required. However, under electrical, mechanical, or thermal abuse conditions a thermal runaway can occur resulting in an uncontrollable increase in pressure and temperature that can lead to fire and/or explosion, and projection of fragments. In this work, the behavior of LIBs under thermal abuse conditions is analyzed. To this purpose, tests on NCA 18,650 cells are performed in a cone calorimeter by changing the radiative heat flux of the conical heater and the State of Charge (SoC) of the cells from full charge to deep discharge. The dependence of SoC and radiative heat flux on the thermal runaway onset is clearly revealed. In particular, a deep discharge determines an earlier thermal runaway of the cell with respect to those at 50% and 100% of SoC when exposed to high radiative heat flux (50 kW/m²). This is due to a mechanism such as an electrical abuse. Cell components before and after tests are investigated using Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy—Energy Dispersive X-ray Spectroscopy (SEM-EDS) and X-ray Diffraction (XRD) to determine the structural, morphological, and compositional changes. It results that the first reaction (423–443 K) that occurs at the anode involves the decomposition of the electrolyte. This reaction justifies the observed earlier venting and thermal runaway of fully charged cells with respect to half-charged ones due to a greater availability of lithium which allows a faster kinetics of the reaction. In the cathode residues, metallic nickel and NO are found, given by decomposition of metal oxide by the rock-salt phase cathode. Full article

The lifetime of the battery significantly influences the acceptance of electric vehicles. Calendar aging contributes to the limited operating lifetime of lithium-ion batteries. Therefore, its consideration in addition to cyclical aging is essential to understand battery degradation. This study consequently examines the same graphite/NCA pouch cell that was the subject of previously published cyclic aging tests. The cells were aged at different temperatures and states of charge. The self-discharge was continuously monitored, and after each storage period, the remaining capacity and the impedance were measured. The focus of this publication is on the correlation of the measurements. An aging correlation is obtained that is valid for a wide range of temperatures and states of charge. The results show an accelerated capacity fade and impedance rise with increasing temperature, following the law of Arrhenius. However, the obtained data do also indicate that there is no path dependency, i.e., earlier periods at different temperature levels do not affect the present degradation rate. A large impact of the storage state of charge at 100% is evident, whereas the influence is small below 80%. Instead of the commonly applied square root of the time function, our results are in excellent agreement with an exponential function. Full article

While silicon anodes hold promise for use in lithium-ion batteries owing to their very high theoretical storage capacity and relatively low discharge potential, they possess a major problem related to their large volume expansion that occurs with battery aging. The resulting stress and strain can lead to mechanical separation of the anode from the current collector and an unstable solid electrolyte interphase (SEI), resulting in capacity fade. Since capacity loss is in part dependent on the cell materials, two different electrodes, Lithium Nickel Oxide or LiNi_0.8Co_0.15Al_0.05O₂ (NCA) and LiNi_1/3Mn_1/3Co_1/3O₂ (NMC 111), were used in combination with silicon to study capacity fade effects using simulations in COMSOL version 5.5. The results of these studies provide insight into the effects of anode particle size and electrolyte volume fraction on the behavior of silicon anode-based batteries with different positive electrodes. It was observed that the performance of a porous matrix of solid active particles of silicon anode could be improved when the active particles were 150 nm or smaller. The range of optimized values of volume fraction of the electrolyte in the silicon anode were determined to be between 0.55 and 0.40. The silicon anode behaved differently in terms of cell time with NCA and NMC. However, NMC111 gave a high relative capacity in comparison to NCA and proved to be a better working electrode for the proposed silicon anode structure. Full article

The current paper evaluates the thermal performance of immersion cooling for an Electric Vehicle (EV) battery module comprised of NCA-chemistry based cylindrical 21700 format Lithium-ion cells. Efficacy of immersion cooling in improving maximum cell temperature, cell’s temperature gradient, cell-to-cell temperature differential, and pressure drop in the module are investigated by direct comparison with a cold-plate-cooled battery module. Parametric analyses are performed at different module discharge C-rates and coolant flow rates to understand the sensitivity of each cooling strategy to important system performance parameters. The entire numerical analysis is performed using a validated $3D$ time-accurate Computational Fluid Dynamics (CFD) methodology in STAR-CCM+. Results demonstrate that immersion cooling due its higher thermal conductance leads to a lower maximum cell temperature and lower temperature gradients within the cells at high discharge rates. However, a higher rate of heat rejection and poor thermal properties of the dielectric liquid results in a much higher temperature non-uniformity across the module. At lower discharge rates, the two cooling methods show similar thermal performance. Additionally, owing to the lower viscosity and density of the considered dielectric liquid, an immersion-cooled battery module performs significantly better than the cold-plate-cooled module in terms of both coolant pressure drop. Full article

Abundantly available SiO₂ (silica) has great potential as an anode material for lithium-ion batteries because it is inexpensive and flexible. However, silicon oxide-based anode material preparation usually requires many complex steps. In this article, we report a facile method for preparing a SiO₂/C composite derived from coal combustion fly ash as an anode material for Li-ion batteries. SiO₂ was obtained by caustic extraction and HCl precipitation. Then, the SiO₂/C composite was successfully obtained by mechanical milling followed by heat treatment. The samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Electrochemical properties were tested using an 18650 cylindrical cell utilizing LiNi_0.8Co_0.15Al_0.05O₂ (NCA) as the counter electrode. Based on the obtained results, the physiochemical characteristics and electrochemical performance, it was determined that SiO₂/C composites were greatly affected by the temperature of heat treatment. The best result was obtained with the SiO₂ content of 10% w/w, heating temperature of 500 °C, initial specific discharge capacity of 586 mAh g⁻¹ at 0.1 C (1 C = 378 mAh g⁻¹), and reversible capacity of 87% after 20 cycles. These results confirmed that the obtained materials had good initial discharge capacity, cyclability, high performance, and exhibited great potential as an anode material for LIBs. Full article

LiNi_0.80Co_0.15Al_0.05O₂ (NCA) as a lithium ion battery cathode material has received attention for its highly specific capacity and excellent low temperature performance. However, the disadvantages of its high surface lithium compound residues and high pH value have influenced its processing performance and limited its application. This paper uses a facile method to modify NCA through LiAlO₂ coating. The results showed that when the molar ratio of Al(NO₃)₃·9H₂O and lithium compound residues at the surface of NCA cathode material was 0.25:1, the pH of the cathode material decreased from 12.70 to 11.80 and the surface lithium compound residues decreased from 3.99% to 1.48%. The NCA cell was charged and discharged for 100 cycles at 1 C in the voltage range of 3.0–4.3 V, to test the capacity retention of NCA. It was found to be as high as 94.67%, which was 5.36% higher than the control NCA cell. The discharge capacity of NCA-0.25-500 °C was 139.8 mAh/g even at 8 C rate, which was 15% higher than the raw NCA. Further research indicated that Al(NO₃)₃·9H₂O reacted with the surface lithium compound residues of NCA and generated LiAlO₂, which improved the NCA electrochemical performance. Full article