{'fraction_non_alphanumeric': 0.02119815668202765, 'fraction_numerical': 0.005529953917050691, 'mean_word_length': 5.033333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
State of Charge Estimation for LFP Battery Using the Hybrid Method

A method to accurately estimate the state of charge (SOC) for LiFePO4 (LFP) batteries is urgently required, to address the issues associated with the increased use of LPF batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network and enhanced particle swarm optimization (EPSO) algorithm for SOC estimating. With a RBF neural network structure, the EPSO algorithm is used to tune the parameters of the RBF neural network, including the centers and widths of the RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using 12.6V, 52Ah LFP batteries under varied discharging condition. The effectiveness of the proposed method is compared with the Coulomb integration method and the back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.