Search Results (96)

Lithium iron phosphate (LFP) batteries have become a popular choice for energy storage, electrified mobility, and plants. All lithium-based batteries produce flammable vent gas as a result of failure through thermal runaway. LFP cells produce less gas by volume than nickel-based cells, but the composition of this gas most often contains less carbon dioxide and more hydrogen. However, when LFP cells fail, they generate lower temperatures, so the vent gas is rarely ignited. Therefore, the hazard presented by a LFP cell in thermal runaway is less of a direct battery fire hazard but more of a flammable gas source hazard. This research identified the constituents and components of the vent gas for different sized LFP prismatic cells when overcharged to failure. This data was used to calculate the maximum homogenous concentration of gas that would be released into a 1.73 m³ test rig and the percentage of the lower explosive limit (LEL). Overcharge experiments were conducted using the same type of cells in the test rig in the presence of remote ignition sources. Ignition and deflagration of the vent gas were possible at concentrations below the theoretical LEL of the vent gas if it was homogeneously mixed. Full article

Exposure of LIB materials to ambient conditions with some level of humidity, either accidentally owing to imperfect fabrication or cell damage, or deliberately due to battery opening operations for analytical or recycling purposes, is a rather common event. As far as humidity-induced damage is concerned, on the one hand the general chemistry is well known, but on the other hand, concrete structural details of these processes have received limited explicit attention. The present study contributes to this field with an investigation centered on the use of Raman spectroscopy for the assessment of structural modifications using common lithium iron phosphate (LFP) and nickel–cobalt–manganese/lithium–manganese oxide (NCM-LMO) cathodes. The impact of humidity has been followed through the observation of differences in Raman bands of pristine and humidity-exposed cathode materials. Vibrational spectroscopy has been complemented with morphological (SEM), chemical (EDS), and electrochemical analyses. We have thus pinpointed the characteristic morphological and compositional changes corresponding to corrosion and active material dissolution. Electrochemical tests with cathodes reassembled in coin cells allowed for the association of specific capacity losses with humidity damaging. Full article

In this study, a selection of active materials were coated onto commercially available intermediate modulus carbon fibers to form and analyze the performance of novel composite cathodes for structural power composites. Various slurries containing polyvinylidene fluoride (PVDF), active material powders, 1-methyl-2-pyrrolidone (NMP) and carbon black (CB) were used to coat carbon fiber tows by immersion. Four active materials—lithium cobalt oxide (LCO), lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC), and lithium nickel cobalt aluminum oxide (NCA)—were individually tested to assess their electrochemical reversibility. The cells were prepared with a polymer separator and liquid electrolytes and assembled in 2025-coin cells. Electrochemical analysis of the cathode materials showed that at C/5 and room temperature the measured capacities ranged from 39.8 Ah kg⁻¹ to 64.7 Ah kg⁻¹ for the LFP and NCA active materials, respectively. The full cells exhibited capacities of 18.1, 23.5, 27.2, and 28.2 Ah kg⁻¹ after 55 cycles for LFP, LCO, NCA, and NMC811, respectively. Finally, visual and elemental analysis were performed via scanning electron microscope (SEM) and energy-dispersive x-ray (EDX) confirming desirable surface coverage and successful transfer of the active materials onto the carbon fiber tows. Full article

In this work, graphite anodes and lithium iron phosphate (LFP) cathodes are used to examine the effects of sodium hexafluorophosphate (NaPF₆) and potassium hexafluorophosphate (KPF₆) electrolyte additives on the formation of the solid electrolyte interphase and the performance of lithium-ion batteries in both half-cell and full-cell designs. The objective is to assess whether these additives may increase cycle performance, decrease irreversible capacity loss, and improve interfacial stability. Compared to the control electrolyte (1.22 M Lithium hexafluorophosphate (LiPF₆)), cells with NaPF₆ and KPF₆ additives produced less SEI products, which decreased irreversible capacity loss and enhanced initial coulombic efficiency. Following the formation of the solid electrolyte interphase, the specific capacity of the control cell was 607 mA·h/g, with 177 mA·h/g irreversible capacity loss. In contrast, irreversible capacity loss was reduced by 38.98% and 37.85% in cells containing KPF₆ and NaPF₆ additives, respectively. In full cell cycling, a considerable improvement in capacity retention was achieved by adding NaPF₆ and KPF₆. The electrolyte, including NaPF₆, maintained 67.39% greater capacity than the LiPF₆ baseline after 20 cycles, whereas the electrolyte with KPF₆ demonstrated a 30.43% improvement, indicating the positive impacts of these additions. X-ray photoelectron spectroscopy verified that sodium (Na⁺) and potassium (K⁺) ions were present in the SEI of samples containing NaPF₆ and KPF₆. While K⁺ did not intercalate in LFP, cyclic voltammetry confirmed that Na⁺ intercalated into LFP with negligible impact on the energy storage of full cells. These findings demonstrate that NaPF₆ and KPF₆ are suitable additions for enhancing lithium-ion battery performance in the popular artificial graphite/LFP system. Full article

There is an increasing demand for the development of efficient and sustainable battery recycling processes. Currently, many recycling processes rely on toxic inorganic acids to recover materials from high-value battery chemistries such as lithium nickel manganese cobalt oxides (NMCs) and lithium cobalt oxide (LCOs). However, as cell manufacturers seek more cost-effective battery chemistries, the value of the spent battery value chain is increasingly diluted by chemistries such as lithium iron phosphate (LFPs). These cheaper alternatives present a difficulty when recycling, as current recycling processes are geared towards dealing with high-value chemistries; thus, the current processes become less economical. To date, much research is focused on treating a single battery chemistry; however, often, the feed material entering a battery recycling facility is contaminated with other battery chemistries, e.g., LFP feed contaminated with NMC, LCO, or LMOs. This research aims to selectively leach various battery chemistries out of a mixed feed material with the aid of a green organic acid, namely oxalic acid. When operating at the optimal conditions (2% solids, 0.25 M oxalic acid, natural pH around 1.15, 25 °C, 60 min), this research has proven that oxalic acid can be used to selectively dissolve 95.58% and 93.57% of Li and P, respectively, from a mixed LFP-NMC mixed feed, all while only extracting 12.83% of Fe and 8.43% of Mn, with no Co and Ni being detected in solution. Along with the high degree of selectivity, this research has also demonstrated, through varying the pH, that the selectivity of the leaching system can be altered. It was determined that at pH 0.5 the system dissolved both the NMC and LFP chemistries; at a pH of 1.15, the LFP chemistry (Li and P) was selectively targeted. Finally, at a pH of 4, the NMC chemistry (Ni, Co and Mn) was selectively dissolved. Full article

This paper reports on the failure of cells with lithium iron phosphate (LFP) chemistry tested under a range of conditions to understand their effect on the volume and composition of gas generated. Cells of the following formats, 26,650, pouch, and prismatic, and capacities ranging from 3 to 230 Ah, were subjected to external heat, overcharge, and nail penetration tests. Gas volume was calculated, and the following gases analysed: H₂, CO₂, CO, CH₄, C₂H₄, C₂H₆, C₃H₆, and C₃H₈. Cells that failed via external heating under inert conditions (N₂ or Ar atmosphere) at 100% state of charge (SoC) typically generated 0.7 L/Ah of gas; overcharged cells, 0.11–0.68 L/Ah; and nail penetration between 0.3 and 0.5 L/Ah. In general, for all test configurations, regardless of atmosphere, the total gas volume contained a 40% concentration of H₂, 15% of CO₂, and the remaining gas consisted of varying concentrations of CO and flammable hydrocarbons. This demonstrates that despite differences in gas volume, the failure gas composition of LFP cells remains similar. Full article

This paper introduces a data-driven methodology for accurately estimating the residual capacity (RC) of lithium iron phosphate (LFP) batteries through a tailored artificial neural network (ANN) architecture. The proposed model integrates a long short-term memory (LSTM) layer with a fully connected layer, leveraging their combined strengths to achieve precise RC predictions. A distinguishing feature of this study is its ability to deliver highly accurate estimates using a limited dataset that was derived from a single cylindrical LFP battery with a 40 Ah capacity and collected during a controlled experimental campaign. Despite the constraints imposed by the dataset size, the ANN demonstrates remarkable performance, underscoring the model’s capability to operate effectively with minimal data. The dataset is partitioned into the training and testing subsets to ensure a rigorous evaluation. Additionally, the robustness of the approach is validated by testing the trained ANN on data from a second battery cell subjected to a distinct aging process, which was entirely unseen during training. This critical aspect underscores the method’s applicability in estimating RC for batteries with varying aging profiles, a key requirement for real-world deployment. The proposed LSTM-based architecture was also benchmarked against a GRU-based model, yielding significantly lower prediction errors. Furthermore, beyond LFP chemistry, the method was tested on a broader NMC dataset comprising seven cells aged under different C-rates and temperatures, where it maintained high accuracy, confirming its scalability and robustness across chemistries and usage conditions. These results advance battery management systems by offering a robust, efficient modeling framework that optimizes battery utilization across diverse applications, even under data-constrained conditions. Full article

Optimizing the charging rate is crucial for enhancing lithium iron phosphate (LFP) battery performance. The substantial heat generation during high C-rate charging poses a significant risk of thermal runaway, necessitating advanced thermal management strategies. This study systematically investigates the coupling mechanism between charging rates and ambient temperatures in overcharge-induced thermal runaway, filling the knowledge gaps associated with multi-indicator thermal management approaches. Through experiments on prismatic LFP cells across five operational conditions (1C/35 °C, 1.5C/5 °C, 1.5C/15 °C, 1.5C/25 °C, and 1.5C/35 °C), synchronized infrared thermography and electrochemical monitoring quantitatively characterize the thermal–electric coupling dynamics throughout overcharge-to-runaway transitions. The experimental findings reveal three key observations: (1) Charge rate and temperature have synergistic amplification effects on triggering thermal runaway. (2) Contrary to intuition, while low-current/high-temperature charging enhances safety versus high-current/high-temperature conditions, low-temperature/high-current charging triggers thermal runaway faster than high-temperature/high-current scenarios. (3) Staged multi-indicator lithium battery thermal runaway warning signals would be more accurate (first peaks > 0.5 °C/s temperature rise rate + >10 V/s voltage drop rate). These findings collectively demonstrate the imperative for next-generation battery management systems integrating real-time ambient temperature compensation with adaptive C-rate control, fundamentally advancing beyond conventional single-variable thermal regulation strategies. Intelligent adaptation is critical for mitigating thermal runaway risks in LFP battery operations. Full article

Batteries have become the main power source in today’s automotive systems. This paper proposes the design of a fast-balancing passive battery management system (BMS) with remote monitoring for the automotive domain. This system is designed for four series-connected lithium iron phosphate (LiFePO4) cells, which are the preferred choice in the automotive industry. The results show that the proposed BMS can monitor the cell voltages with an error lower than 0.12%, and it can perform the balancing operation successfully with maximum currents of 750 mA during both charging and discharging cycles, not only for LiFePO4 cells, but also for lithium-ion (Li-ion) cells. Furthermore, the cell voltages are sent over the controller area network (CAN) interface for remote monitoring. Full article

Accurate and predictive models of lithium-ion batteries are essential for optimizing performance, extending lifespan, and ensuring safety. The reliability of these models depends on the accurate estimation of internal electrochemical and mechanical parameters, many of which are not directly measurable and must be identified via model-based fitting of experimental data. Unlike other parameter-estimation procedures, this study introduces a novel approach that integrates mechanical measurements with electrical data, with a specific application for lithium iron phosphate (LFP) cells. An error analysis—based on the Root Mean Square Error (RMSE) and confidence ellipses—confirms that the inclusion of mechanical measurements significantly improves the accuracy of the identified parameters and the reliability of the algorithm compared to approaches relying just on electrochemical data. Two scenarios are analyzed: in the first, a teardown of the cell provides direct measurements of electrode thicknesses and the number of layers; in the second, these values are treated as additional unknown parameters. In the teardown case, the electrochemical–mechanical approach achieves significantly lower RMSEs and smaller confidence ellipses, proving its superior accuracy and consistency. In the second scenario, while the RMSE values of electrochemical-mechanical model are similar to those of the purely electrochemical one, the smaller ellipses still indicate better consistency and convergence in the parameter estimates. Furthermore, a sensitivity analysis to initial guesses shows that the electrochemical-mechanical approach is more stable, consistently converging to coherent parameter values and confirming its greater reliability. Full article

Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework’s performance enhances EV reliability, supporting the growing electric mobility sector. Full article

This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO₄)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, and longevity. Understanding these changes is crucial for optimizing battery management strategies and ensuring reliable operation across various applications. To analyze these effects, the study utilizes equivalent electrical circuits (EEC) to model the internal behavior of the battery. The individual components of the EEC—such as its resistive, capacitive, and inductive elements—are examined through 3D waveforms, offering a comprehensive visualization of how each parameter responds to cycling. One of the key contributions of this research is the development and implementation of an EEC identification approach that enables a systematic assessment of battery parameter evolution. This technique provides insights into the general trends and variations in electrical behavior based on the state of charge (SoC) of the cell. By analyzing data across a wide range of SoC values—from 0% (fully discharged) to 100% (fully charged)—and tracking changes over 100 charge–discharge cycles, the study highlights the progressive alterations in battery performance. The findings of this investigation offer valuable implications for battery health monitoring, predictive maintenance, and the refinement of state estimation models. Full article

Batteries degrade over time. Such degradation leads to performance loss, but more importantly, safety issues arise. To evaluate the battery degradation, traditional diagnostic techniques rely on model-based or data-driven approaches; however, those methods often require controlled conditions or specific tests, which may not be applicable in real fields. In this regard, we propose a deep learning-based method addressing these limitations by accurately modeling batteries using real-world operational data from photovoltaic (PV)-integrated battery energy storage system (BESSs), where charging currents vary dynamically and SOC is capped at 70% by regulation. The proposed method is based on a neural surrogate model for batteries, employing a sequence-to-sequence architecture, which directly captures the dynamic behavior of batteries from operational data, eliminating the need for specialized characterization tests or feature extraction. The proposed model synthesizes the terminal voltage with a mean absolute error of 6.4 mV for lithium–iron–phosphate (LFP) cells and 49 mV for nickel–cobalt–manganese (NCM) battery modules, respectively, which is only 0.4% and 0.29% of the voltage swing. As a health indicator, we also propose the concept of voltage deviation (VD), defined as the deviation between the synthesized and actual terminal voltages. We demonstrate that VD can be evaluated not only in laboratory data but also in field data. Full article

Accurate estimation of the state of energy (SOE) in lithium-ion batteries is crucial for determining the output power and driving range of electric vehicles. However, in series-connected battery packs, inconsistencies among individual cells pose significant challenges for precise SOE estimation. This issue is particularly pronounced for lithium iron phosphate (LFP) batteries. Their relatively flat open-circuit voltage (OCV) curve makes the classic method of directly weighting the SOE of representative cells—commonly used for ternary batteries—ineffective. This is because the traditional method relies heavily on a linear relationship between the SOE and the voltage, which is not present in LFP batteries. To address this challenge, a novel SOE estimation approach based on the online construction of the battery pack’s comprehensive OCV curve is proposed in this paper. In this new approach, the weighting of representative cells shifts from a result-oriented mode to a key-parameter-oriented mode. By adopting this mode, the whole pack’s comprehensive OCV can be obtained training free and the pack’s SOE can be estimated online within an equivalent circuit model framework. The experimental results demonstrate that the proposed method effectively controls the SOE estimation error within 3% for series battery packs composed of cells with varying degrees of aging. Full article

This paper presents entropy measurements for a large set of commercial Li-ion cells. We present entropy data on full cells with a variety of common Li-ion cell electrode chemistries; graphite, hard carbon, lithium-titanium-oxide (LTO), lithium cobalt-oxide (LCO), nickel manganese cobalt oxides (NMC), nickel cobalt aluminium oxide (NCA), lithium iron-phosphate (LFP), as well as electrodes with mixes of these. All data were collected using an accelerated potentiometric method in steps of approximately 5% State-of-Charge (SoC) across the full SoC window. We observe that the entropy profiles depend on the chemistry of the Li-ion cells, but that they also vary between different commercial cells with the same chemistry. Entropy contributions are quantified with respect to both, their means, positive and negative contributions as well as their SoC variation. In addition, we present how different cyclic ageing temperatures change the entropy profiles for a selected commercial Li-ion cell through ageing. A clear difference in entropy profiles is observed after a capacity loss of 20%. This difference can be attributed to different ageing mechanisms within the Li-ion cells, leading to changes in the balancing of electrodes, as well as changes in the electrode materials. Full article