State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

Abstract

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

“…45 RBF neural network has been used to reduce the RMSE of SOC output prediction to 0.52% at a fixed temperature. 46 In Reference 47, the K-means clustering algorithm was applied to determine the number of hidden layers of the RBF neural network and rationalize the RBF neural network structure with the error reduced to about 0.5%. Since the input variables are crucial to ensure the accuracy of the RBF neural network, the FRA method was used to select the input variables.…”

“…The results showed that the improved RBF model could obtain high estimation accuracy at an acceptable time cost, and the RMSE of the absolute error was almost always within ±0.08%. 48 RBF neural networks can currently achieve close SOC tracking by predicting short-term drive velocity, 49 as shown in Figure 3. The results of SOC estimation based on the RBF neural network are summarized in Table 2.…”

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

“…For example, Chen et al employed a recurrent neural network to estimate the battery SOC [14], and Tian et al utilized a deep neural network to identify the open-circuit voltage of the battery and estimate the SOC [15]. In addition, Zhang et al used a radial basis function (RBF) neural network for battery SOC estimation [16]. Furthermore, Guo et al studied the effects of the key parameters of neural network functions on the battery SOC estimation results [17].…”

The Savitzky‐Golay filter based bidirectional long short‐term memory network for SOC estimation

“…The battery management system (BMS), which is an essential link between batteries and EVs, can closely monitor the status of each cell in the battery pack, intelligently manage the cells, and then extend the service life of them 4‐6 . The most critical parameter for the BMS is the state of charge (SOC), 7,8 but it cannot be measured directly owing to the complex battery structure. Therefore, the battery terminal voltage, charge and discharge current, and internal resistance is often used to estimate SOC.…”