Energy storage lithium battery system soc verification method

What is accurate state of charge (SOC) in lithium-ion batteries?

Accurate state of charge (SOC) constitutes the basis for reliable operations of lithium-ion batteries. The deep learning technique, a game changer in many fields, has recently emerged as a promising solution to accurate SOC estimation, particularly in the era of battery big data consisting of field and testing data.

What is SOC in lithium ion batteries?

SOC is a significant parameter of lithium-ion batteries and indicates the charge level of a battery cell to drive an EV 4, 5. SOC estimation of lithium-ion batteries is compulsory for the safe and efficient operation of EVs. An accurate SOC estimation method improves the battery lifespan by controlling overcharge and overdischarge states 6.

How to estimate SOC of lithium-ion batteries in electric vehicles?

Currently, most studies have focused on the SOC estimation of lithium-ion batteries in electric vehicles (EVs), in which the estimation methods can be classified into three categories, such as ampere-hour counting (AHC), model-based method, and data-driven method .

What is a SoC estimation method for Li-ion batteries?

batteries based on the fusion of deep learning and filtering algorithms. This book proposes a SOC estimation method for Li-ion batteries using bi-directional long and short-term memory neural network (BiLSTM), which avoids the problem that the long and short-term memory neural network (LSTM) can only learn in one direction, resulting

Why do lithium-ion batteries have a high SoC estimation error?

Third, SOC estimation accuracy deteriorates for lithium-ion batteries whose voltage is not sensitive to SOC variation. For example, LiFeO 4 batteries have a flat OCV-SOC relationship, which means that a slight OCV error can cause a high SOC estimation error .

Can deep learning be used for lithium-ion battery state estimation?

Recently, Luo et al. attempts to explore the applications of deep learning in SOC and SOH estimation of lithium-ion batteries. They first analyse the ECMs and electrochemical models before discussing the promising capability of machine learning and deep learning for battery state estimation.