Degradation Mode Knowledge Transfer Method for LFP Batteries

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

“…It is specific to each electrode and is divided into a LAM PE for the positive electrode and a LAM NE for the negative electrode. LAM PE can occur because of structural disorder, dissolution, or loss of electrical contact [82], whereas LAM NE is caused by factors such as particle cracking, loss of electrical contact, or the presence of resistive surface layers that block the active sites of the anode [83]. The degradation associated with the decomposition of the binder or corrosion of the current collector is described by the conductivity loss mode [79].…”

“…Recently, TL has been applied to Li-ion batteries. For example, Lu et al [83] proposed a novel approach for transferring degradation mode (DM) knowledge from synthetic LFP battery datasets to real-world LFP batteries using a deepdomain adaptation approach. The study used a deep CNN architecture composed of a series of residual blocks, called ResNet-50, to classify the DM for the LFP.…”

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

“…Mayilvahanan et al 31 proposed an ML framework, training by the physics‐based synthetic data, 28 to simultaneously quantitatively predict the values of three degradation modes and classify the limiting electrode with the input of preprocessing the low‐rate charging curves. Leveraging the same synthetic dataset, cycle‐to‐cycle evolution of capacity () 119 /IC curves 120 images represented as input features, diagnosis models embedded with NN were trained to quantify the three degradation modes of real‐world LIBs. Kim et al 121 presented a synthetic–data‐based deep learning framework for two‐step classifying dominant aging modes and quantifying the corresponding LLI as well as LAM, with the input of capacity fade and features derived from IC curves.…”

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

“…The energy storage system consists of the EES system and the HES system, which can be described as follows [64,65]:…”

“…The energy storage system consists of the EES system and the HES system, which can be described as follows [64, 65]: $$$$\begin{equation}\left\{ \def\eqcellsep{&}\begin{array}{l} S_t^{{n}^{es}} = S_{t - 1}^{{n}^{es}}\left( {1 - {\zeta }^{es}} \right) + \left( {{\eta }^{c,es}P_t^{c,{n}^{es}} - \dfrac{{P_t^{d,{n}^{es}}}}{{{\eta }^{d,es}}}} \right)\Delta t\\[15pt] S_t^{{n}^{hs}} = S_{t - 1}^{{n}^{hs}}\left( {1 - {\zeta }^{hs}} \right) + \left( {{\eta }^{c,hs}Q_t^{c,{n}^{hs}} - \dfrac{{Q_t^{d,{n}^{hs}}}}{{{\eta }^{d,hs}}}} \right)\Delta t \end{array} \right.\end{equation}$$$$where $$S_t^{{n}^{es}}$$ and $$S_t^{{n}^{hs}}$$ are the electricity storage of the n es th EES unit and the heat storage of the n hs th HES unit; n es = 1,2, …, $${N}^{es}$$, n hs = 1,2, …, $${N}^{hs}$$; …”