Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm

Abstract

Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.

Optimal Scheduling of the Wind-Photovoltaic-Energy Storage Multi-Energy Complementary System Considering Battery Service Life

“…The relationship between the battery life-loss weights and SOC is shown in Equation (10). The mathematical relationship can be plotted, as shown in Figure 2, where the battery SOC is lower than 0.5, its life-loss weight is higher, and the life-loss weight tends to decrease linearly when it is greater than 0.5 [25].…”

“…The mathematical relationship can be plotted, as shown in Figure 2, where the battery SOC is lower than 0.5, its life-loss weight is higher, and the life-loss weight tends to decrease linearly when it is greater than 0.5 [25]. Therefore, the life-loss coefficient of the combined system within a scheduling cycle T is S loss .…”

“…The flow of Energies 2023, 16, 5002 10 of 17 the improved particle swarm algorithm is shown in Figure 6. The equations regarding the learning factors of asynchronous change are as follows: (25) achieving the goal of global optimization; To avoid having particles fall into premature convergence, mutation processing is added to the algorithm.…”

New Topology Design and On-line Charging and Discharging Technology of Substation DC System

“…To ensure efficient current sharing between lead-acid and lithium-ion batteries, the fuzzy logic controller provides the percentage reference current required by the battery and regulates the battery's state of charge within desired limits. In battery monitoring research, Sun et al [7] proposed a method for predicting SOC of lead-acid batteries based on the improved AdaBoost model. By using an online sequence extreme value learning machine as its weak learning machine, this model can realize the incremental learning of the model with high computational efficiency.…”

Impact of Spotted Hyena Optimized Cascade Controller in Load Frequency Control of Wave-Solar-Double Compensated Capacitive Energy Storage Based Interconnected Power System

“…However, the usage of typical methods such as straight pursuit, arbitrary pursuit, incline pursuit, and numerous others is pretty arduous and delivers substandard consequences, as well as craving a great number of repetitions to deliver outcomes. The optimization procedures that have previously been found in AGC knowledge are whale optimization algorithm (WOA) [17], bacterial foraging optimization (BFO) [24], cuckoo search (CS) [26], differential evolution (DE) [32], particle swarm optimization (PSO) [33], firefly algorithm (FA) [34], grey wolf optimization (GWO) [35], imperialist competitive algorithm (ICA) [36], flower pollination algorithm (FPA) [37], honey badger algorithm [38], AdaBoost algorithm [39], and improved mothflame algorithm [40]. A newly developed bio-inspired meta-heuristic algorithm titled spotted hyena optimizer (SHO) [41] is obtainable from the literature.…”