The degradation process of lithium-ion batteries is intricately linked to
their entire lifecycle as power sources and energy storage devices,
encompassing aspects such as performance delivery and cycling utilization.
Consequently, the accurate and expedient estimation or prediction of the aging
state of lithium-ion batteries has garnered extensive attention. Nonetheless,
prevailing research predominantly concentrates on either aging estimation or
prediction, neglecting the dynamic fusion of both facets. This paper proposes a
hybrid model for capacity aging estimation and prediction based on deep
learning, wherein salient features highly pertinent to aging are extracted from
charge and discharge relaxation processes. By amalgamating historical capacity
decay data, the model dynamically furnishes estimations of the present capacity
and forecasts of future capacity for lithium-ion batteries. Our approach is
validated against a novel dataset involving charge and discharge cycles at
varying rates. Specifically, under a charging condition of 0.25C, a mean
absolute percentage error (MAPE) of 0.29% is achieved. This outcome underscores
the model's adeptness in harnessing relaxation processes commonly encountered
in the real world and synergizing with historical capacity records within
battery management systems (BMS), thereby affording estimations and
prognostications of capacity decline with heightened precision.

提出了基于深度学习的混合模型，从充电和放电松弛过程中提取与衰老相关的显著特征，通过合并历史容量衰减数据，动态地提供锂离子电池当前容量的估计和未来容量的预测。验证结果表明，在 0.25C 充电条件下，平均绝对百分比误差（MAPE）为 0.29％，显示了该模型在实际放松过程中的准确性，并与电池管理系统（BMS）中的历史容量记录相结合，从而更准确地预测容量下降。

Cerberus: 基于放松电压曲线的锂离子电池老化估计和预测的深度学习混合模型

Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging  Estimation and Prediction Based on Relaxation Voltage Curves

In recent years, battery technology for electric vehicles (EVs) has been a
major focus, with a significant emphasis on developing new battery materials
and chemistries. However, accurately predicting key battery parameters, such as
state-of-charge (SOC) and temperature, remains a challenge for constructing
advanced battery management systems (BMS). Existing battery models do not
comprehensively cover all parameters affecting battery performance, including
non-battery-related factors like ambient temperature, cabin temperature,
elevation, and regenerative braking during EV operation. Due to the difficulty
of incorporating these auxiliary parameters into traditional models, a
data-driven approach is suggested. Time-series-transformers (TSTs), leveraging
multiheaded attention and parallelization-friendly architecture, are explored
alongside LSTM models. Novel TST architectures, including encoder TST + decoder
LSTM and a hybrid TST-LSTM, are also developed and compared against existing
models. A dataset comprising 72 driving trips in a BMW i3 (60 Ah) is used to
address battery life prediction in EVs, aiming to create accurate TST models
that incorporate environmental, battery, vehicle driving, and heating circuit
data to predict SOC and battery temperature for future time steps.

近年来，电动车辆 (EVs) 的电池技术一直备受关注，重点是开发新的电池材料和化学成分，然而准确预测电池参数，如充电状态 (SOC) 和温度，对于构建先进的电池管理系统 (BMS) 仍然是一项挑战。鉴于将这些辅助参数整合到传统模型中的困难，建议采用数据驱动的方法，通过利用多头注意力和并行化友好架构的时间序列转换器 (TSTs) 和 LSTM 模型研究。还开发了新颖的 TST 架构，包括编码器 TST + 解码器 LSTM 和混合式 TST-LSTM，并与现有模型进行比较。使用一组包含宝马 i3 (60 Ah) 的 72 次行驶记录的数据集来解决 EV 中的电池寿命预测问题，旨在创建准确的 TST 模型，将环境、电池、车辆驾驶和加热电路数据纳入，以预测未来时间步骤的 SOC 和电池温度。

探索不同的时间序列变换器（TST）架构：电动汽车（EVs）电池寿命预测案例研究

Exploring Different Time-series-Transformer (TST) Architectures: A Case  Study in Battery Life Prediction for Electric Vehicles (EVs)

Electrochemical batteries are ubiquitous devices in our society. When they
are employed in mission-critical applications, the ability to precisely predict
the end of discharge under highly variable environmental and operating
conditions is of paramount importance in order to support operational
decision-making. While there are accurate predictive models of the processes
underlying the charge and discharge phases of batteries, the modelling of
ageing and its effect on performance remains poorly understood. Such a lack of
understanding often leads to inaccurate models or the need for time-consuming
calibration procedures whenever the battery ages or its conditions change
significantly. This represents a major obstacle to the real-world deployment of
efficient and robust battery management systems. In this paper, we propose for
the first time an approach that can predict the voltage discharge curve for
batteries of any degradation level without the need for calibration. In
particular, we introduce Dynaformer, a novel Transformer-based deep learning
architecture which is able to simultaneously infer the ageing state from a
limited number of voltage/current samples and predict the full voltage
discharge curve for real batteries with high precision. Our experiments show
that the trained model is effective for input current profiles of different
complexities and is robust to a wide range of degradation levels. In addition
to evaluating the performance of the proposed framework on simulated data, we
demonstrate that a minimal amount of fine-tuning allows the model to bridge the
simulation-to-real gap between simulations and real data collected from a set
of batteries. The proposed methodology enables the utilization of
battery-powered systems until the end of discharge in a controlled and
predictable way, thereby significantly prolonging the operating cycles and
reducing costs.

本文提出了一种能够不需要校准便可预测任何功率下电池电压放电曲线的方法，采用了一种称为 Dynaformer 的新型基于 Transformer 的深度学习架构，能够同时推断出电池的状态和预测真实电池的电压放电曲线，证明该方法能够控制电池高效率地进行可控且可预测的放电，从而延长使用寿命并降低成本。