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In real industrial electronic applications that involve batteries, the inevitable health degradation of batteries would result in both the shorter battery service life and decreased performance. In this article, an attention-based model is proposed for Li-ion battery calendar health prognostics, i.e., the capacity forecaster based on knowledge-data-driven attention (CFKDA), which will be the first work that applies attention mechanism to benefit battery calendar health monitor and management. By taking the battery empirical knowledge as the foundation of its crucial part, i.e., the knowledge-driven attention module, the CFKDA has realized a satisfactory combination of the complementary domain knowledge and data, which has improved both its theoretic strength and prognostic performance significantly. Experimental studies on practical battery calendar ageing demonstrate the superiority of CFKDA in forecasting and generalizing to unwitnessed conditions over both state-of-the-art knowledge-driven and data-driven calendar health prognostic models, implying that the introduction of domain knowledge in CFKDA has brought a significant performance improvement. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

—Batteries are acknowledged as an important carrier for next-generation energy and future electrification. Capacity degradation is an inevitable process that will eventually lead to a decline in battery performance. This paper presents a battery degradation trajectory prognostics approach under storage conditions based on the data-driven method with necessary knowledge inspiration. This data-driven approach is based on Support Vector Regression (SVR), incorporating mechanisms and empirical knowledge of battery calendar aging as the kernel. The developed data-driven approach is evaluated using two real but different capacity degradation datasets: Dataset A has two storage temperatures (25 °C and 45 °C) and storage SoCs (50% and 90%), while Dataset B has the same storage temperatures but four different storage SoCs (20%, 50%, 70% and 95%). The illustrative results show that the developed SVR-based data-driven approach with the battery knowledge-motivated kernel can make accurate future capacity prognostics under different storage conditions. Due to the superiority of its flexibility and knowledge-data-motivation, the developed approach could be extended and help engineers monitor or capture the future capacity degradation trajectory of other types of batteries when their relevant data become available, which in turn will benefit the life management of battery-based energy storage applications. • Knowledge-motivated data-driven method for battery degradation is achieved. • Mechanism knowledge and empirical knowledge are completely coupled. • A long-term capacity degradation trajectory prediction method is proposed. • The methodologies are completely validated based on experiments. [ABSTRACT FROM AUTHOR]

Batteries are presently pervasive in portable electronics, electrified vehicles, and renewable energy storage. These indispensable engineering applications are all safety-critical and energy efficiency-demanding such that batteries must be meticulously monitored and manipulated, where effectively estimating the internal battery states is a key enabler. The primary goal of this paper is to present a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain. More specifically, for the first time, the state of the art in State of Charge (SOC), State of Energy (SOE), State of Health (SOH), State of Power (SOP), State of Temperature (SOT), and State of Safety (SOS) estimation is all elucidated in a tutorial yet systematical way, along with existing issues exposed. In addition, from six different viewpoints, some future important research opportunities and evolving trends of this prosperous field are disclosed, in order to stimulate more technologically innovative breakthroughs in SOC/SOE/SOH/SOP/SOT/SOS estimation. • Critical battery internal states are defined and explained. • Battery state estimation methods are systematically overviewed. • Key issues, technical challenges and opportunities are elucidated. • Future research directions and trends are presented. [ABSTRACT FROM AUTHOR]