Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

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Data-driven prediction of battery cycle life before capacity degradation

Electrochemical capacitors can store electrical energy harvested from intermittent sources and deliver energy quickly, but their energy density must be increased if they are to efficiently power flexible and wearable electronics, as well as larger equipment. This Review summarizes progress in the field of materials for electrochemical capacitors over the past decade as well as outlines key perspectives for future research. We describe electrical double-layer capacitors based on high-surface-area carbons, pseudocapacitive materials such as oxides and the two-dimensional inorganic compounds known as MXenes, and emerging microdevices for the Internet of Things. We show that new nanostructured electrode materials and matching electrolytes are required to maximize the amount of energy and speed of delivery, and different manufacturing methods will be needed to meet the requirements of the future generation of electronic devices. Scientifically justified metrics for testing, comparison and optimization of various kinds of electrochemical capacitors are provided and explained.

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Perspectives for electrochemical capacitors and related devices.

Degradation diagnostics for lithium ion cells

Tremendous efforts are being made to develop electrode materials, electrolytes, and separators for energy storage devices to meet the needs of emerging technologies such as electric vehicles, decarbonized electricity, and electrochemical energy storage. However, the sustainability concerns of lithium-ion batteries (LIBs) and next-generation rechargeable batteries have received little attention. Recycling plays an important role in the overall sustainability of future batteries and is affected by battery attributes including environmental hazards and the value of their constituent resources. Therefore, recycling should be considered when developing battery systems. Herein, we provide a systematic overview of rechargeable battery sustainability. With a particular focus on electric vehicles, we analyze the market competitiveness of batteries in terms of economy, environment, and policy. Considering the large volumes of batteries soon to be retired, we comprehensively evaluate battery utilization and recycling from the perspectives of economic feasibility, environmental impact, technology, and safety. Battery sustainability is discussed with respect to life-cycle assessment and analyzed from the perspectives of strategic resources and economic demand. Finally, we propose a 4H strategy for battery recycling with the aims of high efficiency, high economic return, high environmental benefit, and high safety. New challenges and future prospects for battery sustainability are also highlighted.

Sustainable Recycling Technology for Li-Ion Batteries and Beyond: Challenges and Future Prospects.

In the recent years, lithium-ion batteries have become the battery technology of choice for portable devices, electric vehicles and grid storage. While increasing numbers of car manufacturers are introducing electrified models into their offering, range anxiety and the length of time required to recharge the batteries are still a common concern. The high currents needed to accelerate the charging process have been known to reduce energy efficiency and cause accelerated capacity and power fade. Fast charging is a multiscale problem, therefore insights from atomic to system level are required to understand and improve fast charging performance. The present paper reviews the literature on the physical phenomena that limit battery charging speeds, the degradation mechanisms that commonly result from charging at high currents, and the approaches that have been proposed to address these issues. Special attention is paid to low temperature charging. Alternative fast charging protocols are presented and critically assessed. Safety implications are explored, including the potential influence of fast charging on thermal runaway characteristics. Finally, knowledge gaps are identified and recommendations are made for the direction of future research. The need to develop reliable onboard methods to detect lithium plating and mechanical degradation is highlighted. Robust model-based charging optimisation strategies are identified as key to enabling fast charging in all conditions. Thermal management strategies to both cool batteries during charging and preheat them in cold weather are acknowledged as critical, with a particular focus on techniques capable of achieving high speeds and good temperature homogeneities.

Lithium-ion battery fast charging: A review

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage–time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage–time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2%–3% root-mean-squared error (RMSE).

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

We present an open circuit voltage (OCV) model for lithium ion (Li-ion) cells, which can be parameterized by measurements of the OCV of positive and negative electrode half-cells and a full cell. No prior knowledge of physical parameters related to particular cell chemistries is required. The OCV of the full cell is calculated from two electrode sub-models, which are comprised of additive terms that represent the phase transitions of the active electrode materials. The model structure is flexible and can be applied to any Li-ion cell chemistry. The model can account for temperature dependence and voltage hysteresis of the OCV. Fitting the model to

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A Parametric Open Circuit Voltage Model for Lithium Ion Batteries

Time-domain fitting of battery electrochemical impedance models

We propose a method for dynamic model identification and parameter estimation of LiFePO 4 cells based on current pulse measurements and electrochemical impedance spectroscopy (EIS). Modelling efforts were focused on diffusion as the predominant dynamic process relevant to battery management systems. An equivalent circuit model approach was adopted with parameters dependant on temperature and state of charge (SOC). The model was parameterised at 50 °C, 20 °C, 0 °C and -30 °C and between 10 % and 100 % SOC. Initial parameter estimations for the model identification procedure were informed by EIS. The model was validated (at constant SOC), for the entire temperature range and at C-rates between C/2 and 9C, by voltage simulation based on a dynamic drive cycle profile. Maximal residuals did not exceed 68 mV or 2 % of the nominal cell voltage (V nom ) and root-mean-square deviations remained within 28 mV or 0.8 % of V nom at all temperatures and C-rates.

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Christoph R. Birkl

Papers

Degradation diagnostics for lithium ion cells

Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries

A Parametric Open Circuit Voltage Model for Lithium Ion Batteries

Time-domain fitting of battery electrochemical impedance models

Model identification and parameter estimation for LiFePO 4 batteries