Balancing accuracy and efficiency: a homogeneous ensemble approach for lithium-ion battery state of charge estimation in electric vehicles

In recent years, lithium-ion batteries (LIB) have become the de facto energy storage means for electric vehicles (EVs) due to their high energy density. However, LIBs require state of charge (SOC) monitoring to ensure safe operating conditions and for an enhanced lifespan. Since SOC cannot be directly measured, various estimation methods have been proposed in recent literature, most notably the recent rise in popularity of long short-term memory-recurrent neural networks (LSTM-RNN). Current research in the use of LSTM-RNNs typically applies a single strong data-driven model that can produce accurate predictions at the expense of lengthy model training times. As LSTM-RNNs must be retrained as LIBs age to maintain reasonable estimation accuracies, this poses a problem for EV battery management system processors. To address this research gap, this work proposes a homogeneous ensemble learning model based on several LSTM-RNN base models, as a solution to reduce the training time. The LSTM base models are fused by a meta-learner, to overcome the shortcomings of traditional ensemble fusion methods. Data diversification methods for homogeneous ensembles are also reviewed and benchmarked in this paper. The proposed method achieves a low model training time by 2.6–3.5 times while maintaining a similar mean absolute error (MAE) of 1.4% when compared to conventional shallow and deep LSTM-RNN models. The proposed model was also successfully validated with battery discharge data collected from a custom build battery tester. It is anticipated that the proposed LIB SOC estimation approach can contribute to increased feasibility of using artificial intelligence in EVs in general and improve EV battery management.