基于物理的锂离子电池模型的代理加速参数化
Surrogate-accelerated parameterisation of physics-based Li-ion battery models
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中文总结 AI 辅助
本研究提出基于SPMe模型的代理加速逆框架，利用Artiphy代理快速推断电池参数，以约1 mV误差重现DFN基准电压，并准确恢复电极容量和正极扩散系数，推动快速物理参数化。
中文摘要 AI 辅助
基于物理的锂离子电池模型能够提供具有物理意义的内部电化学状态和过程，但从终端电流-电压数据推断特定电池参数在计算上成本高昂，且受可辨识性限制。我们提出了一种基于单粒子模型（含电解质动力学，SPMe）的代理加速逆框架。其正向映射使用我们的Artiphy代理框架，对电压和选定的内部状态进行快速、可微分的评估。在重新缩放以消除精确的结构冗余后，我们推断非冗余的传输、动力学和容量参数组，包括浓度依赖的固体和电解质扩散系数。来自Doyle-Fuller-Newman（DFN）模型在类似WLTP电流协议下的合成电压数据提供了一个基准，该基准具有已知的参考参数和受控的模型差异。推断出的SPMe以约1 mV的误差重现了基准电压，并很好地恢复了电极容量。正极扩散系数在探测的化学计量范围内的大部分区域被准确恢复。局部灵敏度和Fisher信息分析识别了相关的动力学-欧姆和电解质传输方向，并展示了局部信息和全局扩散系数参数化如何在大部分驾驶循环中对负极扩散的电压敏感性较弱的情况下，产生狭窄的Fisher曲率包络。这些结果代表了向快速基于物理的计算机内参数化以及减少对破坏性电池表征依赖迈出的一步。
英文摘要
Physics-based lithium-ion battery models provide access to physically meaningful internal electrochemical states and processes, but cell-specific parameter inference from terminal current-voltage data is computationally expensive and limited by identifiability. We present a surrogate-accelerated inverse framework based on a single-particle model with electrolyte dynamics (SPMe). Its forward map uses our Artiphy surrogate framework for rapid, differentiable evaluation of voltage and selected internal states. After rescaling to remove exact structural redundancies, we infer non-redundant transport, kinetic and capacity parameter groups, including concentration-dependent solid and electrolyte diffusivities. Synthetic voltage data from a Doyle-Fuller-Newman (DFN) model under a WLTP-like current protocol provide a benchmark with known reference parameters and controlled model discrepancy. The inferred SPMe reproduces the benchmark voltage with an error of order 1 mV and recovers electrode capacities well. Positive-electrode diffusivity is recovered accurately over much of the probed stoichiometric range. Local sensitivity and Fisher-information analysis identifies correlated kinetic-Ohmic and electrolyte-transport directions, and shows how localised information and the global diffusivity parameterisation can yield narrow Fisher-curvature envelopes despite weak voltage sensitivity to negative-electrode diffusion over much of the drive cycle. These results represent a step towards rapid physics-based in-silico parameterisation and reduced reliance on destructive cell characterisation.
