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The traditional neural networks and support vector machine have the weakness of complex mechanism and large amount of computation. It is difficult to track the complex and fast inner reaction of LiFePO₄ battery pack in real time, affecting the estimation accuracy of the battery state of charge. A simple and effective extreme learning machine is proposed for the modeling of pure electric vehicle’s LiFePO₄ battery pack,whose rated capacity is 100 Ah and nominal voltage is 72 V, then compared with the back-propagation neural networks-based, radical basis function neural networks-based and support vector machines-based. Subsequently, taking the learning time and generalization performance as the optimization goal and using the particle swarm to find the optimal hidden node. The results show that the model of LiFePO₄ battery pack based on extreme learning machine has shorter learning time and higher generalization performance compared with the model based on BP neural networks, RBF neural networks and support vector machines. After optimization of hidden nodes, learning time and generalization performance of the model is optimal.