A GRU-RNN based momentum optimized algorithm for SOC estimation

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

“…Similarly for studies of LIBs, a number of data-driven estimation models for Figure 1), comprising a pair of resistors R e , R ct , accounting for the resistance of the electrolyte and current collector foils, capacitor C dl for charge transfer effects and electrical double-layers, and a Warburg impedance Z W element representing diffusion. 35 the battery SoC, 15,39,40 SoH 12,[15][16][17][18]41 and RUL have been developed. 12,42,43 Many of these studies focus on online estimation of SoH by capacity, with the prevailing approach involving following the evolution of the current, terminal voltage and partial capacity curves of the battery with time as applied by previous works.…”

“…Such an approach is applied in Eddahech et al 12 monitors the degradation of capacity and internal resistance with time to develop a high-accuracy SoH degradation model. More recently, similar approaches have been explored based on the RNN principle by monitoring the cell current and terminal voltage, including application of the gated recurrent unit (GRU) RNN by Jiao et al 39 and long short-term memory (LSTM) in Mamo and Wang 40 for the related problem of SoC estimation, as well as a modified LSTM in Li et al 43 for estimation of SoH. Generally, the RNN-based approaches are capable of predicting the battery states with outstanding accuracy and can be extended in the case of SoH to predict the evolution of the battery condition for estimation of RUL as in Eddahech et al 12 and Li et al 43 However, being suited for state monitoring, a key limitation is the sensitivity of model predictions to the previous battery state, which makes these approaches unsuitable for characterisation of most end of life batteries.…”

“…For example, data-driven approaches have been applied with success in the related area of bearing fault diagnosis and prognostics, 37,38 where it is often impractical to develop precise health degradation models to predict their RUL. Similarly for studies of LIBs, a number of data-driven estimation models for the battery SoC, 15,39,40 SoH 12,15–18,41 and RUL have been developed. 12,42,43 Many of these studies focus on online estimation of SoH by capacity, with the prevailing approach involving following the evolution of the current, terminal voltage and partial capacity curves of the battery with time as applied by previous works.…”

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

“…e evaluation of the parameters such as the state of charge and the remaining useful life has guiding significance for the use, maintenance, and economic analysis of lithium batteries. e SOC of the compound energy storage system of electric vehicles is the basis of rational energy management [49][50][51], so accurate SOC information is of great significance to improve the dynamic performance [52,53] and range of electric vehicles [54][55][56][57][58].…”

A Novel Input Variable Selection and Structure Optimization Algorithm for Multilayer Perceptron-Based Soft Sensors

“…Due to their powerful nonlinear modeling competence, ANNs have become the most popular nonlinear modeling techniques. ere are a variety of ANNs such as convolutional neural networks (CNN) [13], generative adversarial networks (GAN) [14], radial basis networks, and recurrent neural network (RNN) [15], each of which has its own characteristics and advantages. Among them, multilayer perceptron (MLP) is the most widely used technique for nonlinear soft sensing owing to its outstanding nonlinear mapping capability and convenience of application.…”