鋰離子電池（LIB）是一種鋰離子在正負(fù)極之間交換的電化學(xué)儲能裝置。LIB的正極是該電池中最昂貴的組件，占該電池生產(chǎn)總成本的50%以上。與鎳和鈷基陰極相比，磷酸鐵（LiFePO₄, 也稱為LFP）的陰極具有優(yōu)越的熱和化學(xué)穩(wěn)定性，在高溫下不會發(fā)生分解，是一種更安全的陰極材料。

鈷和鎳的缺失為電池供應(yīng)鏈的可持續(xù)性提供了一條途徑，并有助于創(chuàng)建一個道德能源市場。LFP電池通常使用LiFePO₄和石墨作為正負(fù)電極活性材料。正極的兩相變區(qū)域會共存富鋰相Li_βFePO₄和一個貧鋰相Li_αFePO₄，進(jìn)而導(dǎo)致一個平坦的開路電壓曲線（OCV，正負(fù)電極的開路電位之間的差值）。

Fig. 2 Battery modeling and phase transitions.

平坦的OCV曲線由于缺乏從電壓輸出測量中對系統(tǒng)的可觀測性，從而使估計電荷狀態(tài)（SOC）的任務(wù)具有挑戰(zhàn)性。此外，兩相變還伴隨著明顯的滯后和路徑行為依賴行為，即對于相同的SOC，電池會根據(jù)充電或放電而松弛到不同的OCV值，這給電池管理系統(tǒng)（BMS）策略的設(shè)計帶來了額外的挑戰(zhàn)。

為了設(shè)計有效且高性能的BMS，來自美國斯坦福大學(xué)能源科學(xué)與工程系的Simona Onori教授團(tuán)隊，開發(fā)了一種混合模型，將基于物理的建模優(yōu)勢與機器學(xué)習(xí)模型描述未知物理的能力相結(jié)合，以捕捉瞬態(tài)操作中的滯后和路徑依賴行為。

在該混合模型中，基于物理的模型是通過平均核–殼的增強單粒子模型來描述石墨陽極電池中磷酸鐵正極的兩相變操作，機器學(xué)習(xí)部分基于對電池在不同充放電模式下的電化學(xué)內(nèi)部狀態(tài)的了解，從模型特征和實驗中學(xué)習(xí)滯后行為，以補償模型的不確定性，并分別對19和15小時的電動汽車的真實駕駛輪廓進(jìn)行訓(xùn)練和驗證。

Fig. 5 Hybrid model energetic analysis.

作者的提出的混合模型以數(shù)據(jù)為中心來開發(fā)基于機器學(xué)習(xí)的偽滯后模型，減少了實驗時間，創(chuàng)建了高信息行和低維的數(shù)據(jù)集，對電池性能分析、合成數(shù)據(jù)生成以及開發(fā)用于BMS設(shè)計的降階模型具有重要意義。該文近期發(fā)布于npj?Computational Materials?10:?14 (2024).

Editorial Summary

Lithium-ion batteries (LIBs) are electrochemical energy storage devices where lithium ions exchange between the positive and negative electrodes. The positive electrode of a LIB is the most expensive component of the cell, accounting for more than 50% of the total cell production cost. Compared to nickel- and cobalt-based cathodes, the lithium iron phosphate (LiFePO₄, also referred to as LFP) cathodes offer superior thermal and chemical stability, resulting in a safer cathode material that does not decompose at high temperatures. The absence of cobalt and nickel suggests a pathway for a resilient battery supply chain and contributes to the creation of an ethical energy market. LFP batteries typically use LiFePO₄and graphite as positive and negative electrode active materials, respectively. The two-phase transition region of the positive electrode coexists with a lithium-rich phase (Li_βFePO₄) and a lithium-poor phase (Li_αFePO₄), leading to a flat open-circuit voltage (OCV) curve, defined as the difference between the open circuit potentials of the positive and negative electrodes. The flat OCV curve makes the task of estimating the state of charge (SOC) challenging as it causes a lack of observability of the system’s states from the voltage output measurements. Moreover, the two-phase transition is accompanied by pronounced hysteresis and path dependence behavior, meaning that for the same SOC, the battery may relax to different OCV values depending on whether it is being charging or discharging, posing additional challenges for the design of battery management system (BMS) strategies.?

To design an effective and high-performance BMS, a team led by Prof. Simona Onori from Energy Science and Engineering, Stanford University, developed a hybrid model, combining the advantages of physics-based modeling with the ability of machine learning to describe unknown physics, to capture the hysteresis and path-dependent behavior during transient operation. In this hybrid model, the physics-based model describes the two-phase transition operation of the LFP positive electrode through an averaged core-shell enhanced single particle model, while the machine learning component is based on the understanding of the electrochemical internal states of the battery during different charge and discharge operation over several driving profits. It learns the hysteresis behavior from simulated features and experiments to compensate for model uncertainty, and is trained and validated on real-world driving profiles of 19 and 15 hours for electric vehicles, respectively. The hybrid model shown in this study presents a machine-learning-based pseudo-hysteresis model, reducing experimental time, creating high-information-density and low-dimensional datasets. It is of great significance for battery performance analysis, synthetic data generation, and the development of reduced-order models for BMS design.

This?article was recently?published in?npj?Computational Materials?10:?14?(2024).

原文Abstract及其翻譯

Accelerating the transition to cobalt-free batteries: a hybrid model for LiFePO₄/graphite chemistry (加速向無鈷電池過渡：LiFePO₄/石墨化學(xué)中的混合模型)

Gabriele Pozzato,?Xueyan Li,?Donghoon Lee,?Johan Ko?&?Simona Onori?

Abstract The increased adoption of lithium-iron-phosphate batteries, in response to the need to reduce the battery manufacturing process’s dependence on scarce minerals and create a resilient and ethical supply chain, comes with many challenges. The design of an effective and high-performing battery management system (BMS) for such technology is one of those challenges. In this work, a physics-based model describing the two-phase transition operation of an iron-phosphate positive electrode—in a graphite anode battery—is integrated with a machine-learning model to capture the hysteresis and path-dependent behavior during transient operation. The machine-learning component of the proposed “hybrid” model is built upon the knowledge of the electrochemical internal states of the battery during charge and discharge operation over several driving profiles. The hybrid model is experimentally validated over 15 h of driving, and it is shown that the machine-learning component is responsible for a small percentage of the total battery behavior (i.e., it compensates for voltage hysteresis). The proposed modeling strategy can be used for battery performance analysis, synthetic data generation, and the development of reduced-order models for BMS design.

摘要 隨著對減少電池制造過程中稀有礦物的依賴及創(chuàng)建有韌性和道德供應(yīng)鏈的需求增加，采用磷酸鋰電池面臨著諸多挑戰(zhàn)。針對這種技術(shù)，設(shè)計一個有效且高性能的電池管理系統(tǒng)（BMS）是這些挑戰(zhàn)之一。在這項工作中，一個基于物理的模型被用來描述石墨陽極電池中磷酸鐵正極的兩相變操作，該模型與機器學(xué)習(xí)模型相結(jié)合，以捕捉瞬態(tài)操作中的滯后和路徑依賴行為。我們提出的“混合”模型的機器學(xué)習(xí)部分，建立在對電池在多個驅(qū)動模式下充放電過程中電化學(xué)內(nèi)部狀態(tài)的了解之上。該混合模型經(jīng)過15小時的驅(qū)動實驗驗證，結(jié)果表明，機器學(xué)習(xí)部分僅占電池總行為的一小部分（即，它補償了電壓滯后）。本工作提出的模型策略可用于電池性能分析、合成數(shù)據(jù)生成以及開發(fā)用于BMS設(shè)計的降階模型。