A novel deep time‐index framework with limited data and feature extraction for battery capacity degradation prediction

Abstract

Accurate capacity estimation and degradation trajectory predictions are crucial part of effectively monitoring the current and future health status of lithium‐ion batteries, which ensures their optimal performance and longevity. However, it is hard to acquire precise predictions under various application conditions when relying on limited historical data. In this paper, we propose a novel deep time‐index framework based on meta‐learning to achieve online capacity estimation and degradation trajectory predictions with only a few monitoring data from early cycles. Highly correlated health feature from lithium‐ion battery degradation data is extracted for online capacity estimation. The deep time‐index framework consists of three parts, Implicit Neural Representations (INRs), Long Short‐Term Memory (LSTM) and Ridge regressor. Cycle numbers are selected as time feature and fed into the INRs to obtain informative representations. A LSTM module is employed to process the temporal representations and capture the long‐term dependencies to Ridge regressor to generate a direct multi‐step predictions. Three distinct lithium‐ion battery degradation datasets are applied to validate the efficacy of the proposed approach. The experimental results validate that within the deep time‐index framework, the short‐term predictions show remarkable performance. This high level of accuracy makes the proposed deep time‐index framework suitable for a wide range of real‐world scenarios, as it demonstrates precise predictions even under varying charging and discharging conditions with limited historical data.

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

“…With the outstanding advantages such as high output voltage, high energy density, good cycle performance, small self-discharge and no memory effect, Lithium-ion batteries are an important constituent of global clean-energy, which play the role of core energy storage components in many fields, such as aerospace aircraft, navigation, smart grid, medical, electric vehicles and so on. [1][2][3] However, the charge-discharge reaction of lithium-ion batteries is complicated. With the use of batteries, their internal performance will gradually age and decline.…”

“…The development and use of renewable clean energy to replace fossil energy has become the trend of world energy production and consumption. With the outstanding advantages such as high output voltage, high energy density, good cycle performance, small self‐discharge and no memory effect, Lithium‐ion batteries are an important constituent of global clean‐energy, which play the role of core energy storage components in many fields, such as aerospace aircraft, navigation, smart grid, medical, electric vehicles and so on 1–3 …”

Multifeature‐based online remaining useful life prediction of lithium‐ion batteries in stages using cascaded data‐driven algorithm

“…Before the battery capacity accesses the failure threshold, it must be replaced in advance to avoid safety accidents. 3 Therefore, accurately predicting RUL is essential to ensure the stable and secure operation of lithium-ion batteries and prevent safety incidents.…”