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

The lithium ion battery is widely used in electric vehicles (EV) The battery degradation is the key scientific problem in battery research The battery aging limits its energy storage and power output capability, as well as the performance of the EV including the cost and life span Therefore, a comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper Firstly, the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic Then, to get better life performance, the influence factors affecting battery life are discussed in detail from the perspectives of design, production and application Finally, considering the difference between the cell and system, the battery system degradation mechanism is discussed

A review on the key issues of the lithium ion battery degradation among the whole life cycle

Rechargeable lithium-ion batteries are promising candidates for building grid-level storage systems because of their high energy and power density, low discharge rate, and decreasing cost. A vital aspect in energy storage planning and operations is to accurately model the aging cost of battery cells, especially in irregular cycling operations. This paper proposes a semi-empirical lithium-ion battery degradation model that assesses battery cell life loss from operating profiles. We formulate the model by combining fundamental theories of battery degradation and our observations in battery aging test results. The model is adaptable to different types of lithium-ion batteries, and methods for tuning the model coefficients based on manufacturer’s data are presented. A cycle-counting method is incorporated to identify stress cycles from irregular operations, allowing the degradation model to be applied to any battery energy storage (BES) applications. The usefulness of this model is demonstrated through an assessment of the degradation that a BES would incur by providing frequency control in the PJM regulation market.

Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment

Temperature effect and thermal impact in lithium-ion batteries: A review

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries

A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries

Systematic aging of commercial LiFePO4|Graphite cylindrical cells including a theory explaining rise of capacity during aging

1Electrochemical Energy Conversion and Storage Systems Group, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, 52066 Aachen, Germany 2Jülich Aachen Research Alliance, JARA-Energy, Germany 3Helmholtz Institute Münster (HI MS), IEK-12, Forschungszentrum Jülich, 48149 Münster, Germany 4Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, Aachen, Germany

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Full Cell Parameterization of a High-Power Lithium-Ion Battery for a Physico-Chemical Model: Part I. Physical and Electrochemical Parameters

As lithium-ion batteries play an important role for the electrification of mobility due to their high power and energy density, battery lifetime prediction is a fundamental aspect for successful market introduction. This work shows the development of a lifetime prediction model based on accelerated aging tests. To investigate the impact of different voltages and temperatures on capacity loss and resistance increase, calendar life tests were performed. Additionally, several cycle aging tests were performed using different cycle depths and mean SOC. Both the calendar and the cycle test data were analyzed to find mathematical equations that describe the aging dependence on the varied parameters. Using these functions an aging model coupled to an impedance-based electrical-thermal model was built. The lifetime prognosis model allows analyzing and optimizing different drive cycles and battery management strategies. The cells modeled in this work were thoroughly tested taking into account a wide range of influence factors. As validation tests on realistic driving profiles show, a robust foundation for simulation results is granted. Together with the option of using temperature profiles changing over the seasons, this tool is able to simulate battery aging in various applications.

Johannes Schmalstieg

Papers

Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries

A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries

Systematic aging of commercial LiFePO4|Graphite cylindrical cells including a theory explaining rise of capacity during aging

Full Cell Parameterization of a High-Power Lithium-Ion Battery for a Physico-Chemical Model: Part I. Physical and Electrochemical Parameters

From accelerated aging tests to a lifetime prediction model: Analyzing lithium-ion batteries