基于耦合动力学与多因素老化模型的储能单元放电时间预测框架
A Framework for Discharge Time Prediction of Energy Storage Units Based on Coupled Dynamics and Multi-Factor Aging Models
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- Panjin Campus of Dalian University of Technology（大连理工大学盘锦校区）
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中文总结 AI 辅助
提出一种物理可解释的框架，耦合负载分解、电学闭合、半经验老化及SOC-温度动力学，预测便携设备放电至空时间，实验验证了其可行性与可解释性。
中文摘要 AI 辅助
本文提出了一种物理可解释的框架，用于预测便携式嵌入式系统中的放电至空时间（TTE）。该框架耦合了使用驱动的负载功率分解、电功率-电压-电流闭合、半经验老化模型以及SOC-温度动力学。通过可解释的负载模型和转换效率校正，将智能手机遥测数据映射到电池电流。电池容量损失通过结合阿伦尼乌斯温度依赖性、SEI扩散行为以及循环相关的幂律退化来建模。随后，耦合动态模型在不同初始SOC值、环境温度和使用场景下预测TTE。在6.9小时智能手机放电会话上的时间顺序留出法评估显示，电流RMSE为0.0095 ± 0.0006 A，温度RMSE为2.93 ± 0.24°C，TTE MAPE为4.81 ± 0.61%。在NASA电池B0005上的评估产生了0.031 Ah的容量损失RMSE。基线、消融和反事实分析进一步说明了热和老化校正的贡献以及负载特征的相对影响。结果证明了所提出框架的可行性和可解释性，而跨设备和电池的更广泛验证仍然是必要的。
英文摘要
This paper presents a physically interpretable framework for predicting time to empty (TTE) in portable embedded systems. The framework couples usage-driven load-power decomposition, electrical power-voltage-current closure, a semi-empirical aging model, and SOC-temperature dynamics. Smartphone telemetry is mapped to battery current through an interpretable load model and conversion-efficiency correction. Battery capacity loss is modeled by combining Arrhenius temperature dependence, SEI diffusion behavior, and cycle-related power-law degradation. The coupled dynamic model then predicts TTE under different initial SOC values, ambient temperatures, and usage profiles. Chronological hold-out evaluation on a 6.9-h smartphone discharge session yielded a current RMSE of 0.0095 $\pm$ 0.0006 A, a temperature RMSE of 2.93 $\pm$ 0.24$^\circ$C, and a TTE MAPE of 4.81 $\pm$ 0.61%. Evaluation on NASA cell B0005 produced a capacity-loss RMSE of 0.031 Ah. Baseline, ablation, and counterfactual analyses further illustrate the contributions of thermal and aging corrections and the relative influence of load features. The results demonstrate the feasibility and interpretability of the proposed framework, while broader validation across devices and batteries remains necessary.
