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The 2019 Nobel Prize in Chemistry has been awarded to John B. Goodenough, M. Stanley Whittingham and Akira Yoshino for their contributions in the development of lithium-ion batteries, a technology that has revolutionized our way of life. Here we look back at the milestone discoveries that have shaped the modern lithium-ion batteries for inspirational insights to guide future breakthroughs.

Although the closed pore structure plays a key role in contributing low-voltage plateau capacity of hard carbon anode for sodium-ion batteries, the formation mechanism of closed pores is still under debate. Here, we employ waste wood-derived hard carbon as a template to systematically establish the formation mechanisms of closed pores and their effect on sodium storage performance. We find that the high crystallinity cellulose in nature wood decomposes to long-range carbon layers as the wall of closed pore, and the amorphous component can hinder the graphitization of carbon layer and induce the crispation of long-range carbon layers. The optimized sample demonstrates a high reversible capacity of 430 mAh g −1 at 20 mA g −1 (plateau capacity of 293 mAh g −1 for the second cycle), as well as good rate and stable cycling performances (85.4% after 400 cycles at 500 mA g −1 ). Deep insights into the closed pore formation will greatly forward the rational design of hard carbon anode with high capacity. It is essential to investigate the formation mechanism of closed pore, which contributes to low-voltage plateau capacity of hard carbon anode in sodium ion batteries. Herein, the authors explore the impact of wood precursor components and carbonization temperature on closed pore formation in hard carbon for enhanced battery performance.

Lithium-ion batteries with nickel-rich layered oxide cathodes and graphite anodes have reached specific energies of 250–300 Wh kg −1 (refs. 1 , 2 ), and it is now possible to build a 90 kWh electric vehicle (EV) pack with a 300-mile cruise range. Unfortunately, using such massive batteries to alleviate range anxiety is ineffective for mainstream EV adoption owing to the limited raw resource supply and prohibitively high cost. Ten-minute fast charging enables downsizing of EV batteries for both affordability and sustainability, without causing range anxiety. However, fast charging of energy-dense batteries (more than 250 Wh kg − 1 or higher than 4 mAh cm − 2 ) remains a great challenge 3 , 4 . Here we combine a material-agnostic approach based on asymmetric temperature modulation with a thermally stable dual-salt electrolyte to achieve charging of a 265 Wh kg − 1 battery to 75% (or 70%) state of charge in 12 (or 11) minutes for more than 900 (or 2,000) cycles. This is equivalent to a half million mile range in which every charge is a fast charge. Further, we build a digital twin of such a battery pack to assess its cooling and safety and demonstrate that thermally modulated 4C charging only requires air convection. This offers a compact and intrinsically safe route to cell-to-pack development. The rapid thermal modulation method to yield highly active electrochemical interfaces only during fast charging has important potential to realize both stability and fast charging of next-generation materials, including anodes like silicon and lithium metal. A new approach to charging energy-dense electric vehicle batteries, using temperature modulation with a dual-salt electrolyte, promises a range in excess of 500,000 miles using only rapid (under 12 minute) charges.

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation. Reliable lithium-ion battery health assessment is vital for safety. Here, authors present a physics-informed neural network for accurate and stable state-of-health estimation, overcoming challenges of varied battery types and usage conditions.

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi 0.86 Co 0.11 Al 0.03 O 2 -based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi 0.83 Co 0.11 Mn 0.07 O 2 -based positive electrodes and batteries with the blend of Li(NiCoMn)O 2 - Li(NiCoAl)O 2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation. Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.

Concentrated electrolytes usually demonstrate good electrochemical performance and thermal stability, and are also supposed to be promising when it comes to improving the safety of lithium-ion batteries due to their low flammability. Here, we show that LiN(SO 2 F) 2 -based concentrated electrolytes are incapable of solving the safety issues of lithium-ion batteries. To illustrate, a mechanism based on battery material and characterizations reveals that the tremendous heat in lithium-ion batteries is released due to the reaction between the lithiated graphite and LiN(SO 2 F) 2 triggered thermal runaway of batteries, even if the concentrated electrolyte is non-flammable or low-flammable. Generally, the flammability of an electrolyte represents its behaviors when oxidized by oxygen, while it is the electrolyte reduction that triggers the chain of exothermic reactions in a battery. Thus, this study lights the way to a deeper understanding of the thermal runaway mechanism in batteries as well as the design philosophy of electrolytes for safer lithium-ion batteries. Concentrated electrolytes display superior thermal stability due to their non-flammability nature. Here, the authors show that LiN(SO 2 F) 2 -based concentrated electrolytes are incapable of solving the safety issues due to heat release during reaction between the lithiated graphite and electrolyte.

Lithium-ion batteries have aided the portable electronics revolution for nearly three decades. They are now enabling vehicle electrification and beginning to enter the utility industry. The emergence and dominance of lithium-ion batteries are due to their higher energy density compared to other rechargeable battery systems, enabled by the design and development of high-energy density electrode materials. Basic science research, involving solid-state chemistry and physics, has been at the center of this endeavor, particularly during the 1970s and 1980s. With the award of the 2019 Nobel Prize in Chemistry to the development of lithium-ion batteries, it is enlightening to look back at the evolution of the cathode chemistry that made the modern lithium-ion technology feasible. This review article provides a reflection on how fundamental studies have facilitated the discovery, optimization, and rational design of three major categories of oxide cathodes for lithium-ion batteries, and a personal perspective on the future of this important area. The 2019 Nobel Prize in Chemistry has been awarded to a trio of pioneers of the modern lithium-ion battery. Here, Professor Arumugam Manthiram looks back at the evolution of cathode chemistry, discussing the three major categories of oxide cathode materials with an emphasis on the fundamental solid-state chemistry that has enabled these advances.

The use of state-of-the-art Ni-rich layered oxides (LiNi x Co y Mn 1− x − y O 2 , x  > 0.5) as the cathode material for lithium-ion batteries can push the energy and power density to a higher level than is currently available 1 , 2 . However, volume variation associated with anisotropic lattice strain and stress that is being developed during lithium (de)intercalation induces severe structural instability and electrochemical decay of the cathode materials, which is amplified further when the battery is operating at a high voltage (above 4.5 V), which is essential for unlocking its high energy 3 – 6 . Even after much effort by the research community, an intrinsic strain-retardant method for directly alleviating the continuous accumulation of lattice strain remains elusive. Here, by introducing a coherent perovskite phase into the layered structure functioning as a ‘rivet’, we significantly mitigate the pernicious structural evolutions by a pinning effect. The lattice strain evolution in every single cycle is markedly reduced by nearly 70% when compared with conventional materials, which significantly enhances morphological integrity leading to a notable improvement in battery cyclability. This strain-retardant approach broadens the perspective for lattice engineering to release the strain raised from lithium (de)intercalation and paves the way for the development of high-energy-density cathodes with long durability. The introduction of a coherent perovskite phase into the layered structure of a lithium-ion battery reduces lattice strain and stress to produce a robust crystal structure.

Operando monitoring of complex physical and chemical activities inside rechargeable lithium-ion batteries during thermal runaway is critical to understanding thermal runaway mechanisms and giving early warning of safety-related failure. However, most existing sensors cannot survive during such extremely hazardous thermal runaway processes (temperature up to 500 °C accompanied by fire and explosion). To address this, we develop a compact and multifunctional optical fiber sensor (12 mm in length and 125 µm in diameter) capable of insertion into commercial 18650 cells to continuously monitor internal temperature and pressure effects during cell thermal runaway. We observe a stable and reproducible correlation between the cell thermal runaway and the optical response. The sensor’s signal shows two internal pressure peaks corresponding to safety venting and initiation of thermal runaway. Further analysis reveals that a scalable solution for predicting imminent thermal runaway is the detection of the abrupt turning range of the differential curves of cell temperature and pressure, which corresponds to an internal transformation between the cell reversible and irreversible reactions. By raising an alert even before safety venting, this new operando measurement tool can provide crucial capabilities in cell safety assessment and warning of thermal runaway.

The operation of high-energy all-solid-state lithium-metal batteries at low stack pressure is challenging owing to the Li dendrite growth at the Li anodes and the high interfacial resistance at the cathodes 1 – 4 . Here we design a Mg 16 Bi 84 interlayer at the Li/Li 6 PS 5 Cl interface to suppress the Li dendrite growth, and a F-rich interlayer on LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes to reduce the interfacial resistance. During Li plating–stripping cycles, Mg migrates from the Mg 16 Bi 84 interlayer to the Li anode converting Mg 16 Bi 84 into a multifunctional LiMgS x –Li 3 Bi–LiMg structure with the layers functioning as a solid electrolyte interphase, a porous Li 3 Bi sublayer and a solid binder (welding porous Li 3 Bi onto the Li anode), respectively. The Li 3 Bi sublayer with its high ionic/electronic conductivity ratio allows Li to deposit only on the Li anode surface and grow into the porous Li 3 Bi sublayer, which ameliorates pressure (stress) changes. The NMC811 with the F-rich interlayer converts into F-doped NMC811 cathodes owing to the electrochemical migration of the F anion into the NMC811 at a high potential of 4.3 V stabilizing the cathodes. The anode and cathode interlayer designs enable the NMC811/Li 6 PS 5 Cl/Li cell to achieve a capacity of 7.2 mAh cm −2 at 2.55 mA cm −2 , and the LiNiO 2 /Li 6 PS 5 Cl/Li cell to achieve a capacity of 11.1 mAh cm −2 with a cell-level energy density of 310 Wh kg −1 at a low stack pressure of 2.5 MPa. The Mg 16 Bi 84 anode interlayer and F-rich cathode interlayer provide a general solution for all-solid-state lithium-metal batteries to achieve high energy and fast charging capability at low stack pressure. The inclusion of a Mg–Bi-based interlayer between the lithium metal and solid electrolyte and a F-rich interlayer on the cathode improves the stability and performance of solid-state lithium-metal batteries.