State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Abstract

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

“…To have a complex function, using a simple sigmoid neuron model as the basic building block would not predict the output with high accuracy. Instead, combining several such sigmoid neurons in various layers, as shown in Figure 2 (known as Deep Neural Network (DNN)), can approximate a complex function between input and output [23,24]. The DNN would be differentiable, as the basic block is differentiable to learn the model parameters.…”

Optimal Economic–Environmental Operation of BESS in AC Distribution Systems: A Convex Multi-Objective Formulation

“…According to [11], the type-A battery is installed at node 14, the type-B battery at node 31, and the type-C battery at node 6, respectively. It is important to note that, in order to preserve the useful life of these batteries, their states of charge must be maintained within a range of 10% to 90%, based on experimental data reported in [40].…”

An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine

“…In the literature on machine learning techniques, the number of artificial neural networks appears to be the most popular due to their strong ability to fit, classify, and predict LiB RULs. For example, Hossain et al [7] proposed a method that uses a timedelay neural network (TDNN) to estimate the state of charge (SOC) of LiB. Furthermore, Wu et al [8] provided a method that combines a feed forward neural network (FFNN) with importance sampling to predict the RUL of LiB.…”

“…Additionally, Ren et al [9] and Chemali et al [10] used the deep learning approach to extract features of LiB and then predicted the RUL of LiBs based on these features. Because the weights of the neural network in references [7][8][9][10] are adjusted via continuous iteration, if many data are used, the time complexity will be sufficiently high, leading to a decrease in the accuracy of the RUL prediction. To decrease the time complexity, the extreme learning machine (ELM) can be used to predict the RUL of the LiB.…”