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• W4384306254 abstract "Lithium-ion battery (LiB) packs are commonly used for Electric Vehicle (EV) applications. However, accurate battery pack State of Charge (SOC) estimation is crucial for optimal driving experience. Artificial Neural Networks (ANN) are explored in recent years for SOC estimation, due to its capability to efficiently analyze the non-linear relationship between SOC and temperature, as well as charge/discharge currents. Parallel ANNs (PANNs) are widely used in various applications due to their superior efficiency, which is yet to be explored extensively for SOC estimation. Nevertheless, current PANN designs need large datasets to effectively study the nonlinear battery behaviour. Moreover, present battery pack SOC estimation techniques proposed in recent literature are either found to be complex to implement, or not properly represent the cells that are near extreme operational conditions. Thus, this work proposes a novel PANN architecture for LiB cell SOC estimation, which is achieved through intensive investigation on using multiple Bidirectional Long-Short Term Memory (BiLSTM) based parallel layers, activation functions and grouping of input features (voltage and current, with their derivatives and temperature). When trained and tested with a publicly available dataset, the proposed LiB cell SOC estimator outperformed the conventional recurrent ANNs by 1.5 to 3 times. In addition, a feasible battery pack SOC estimation technique is also investigated based on the proposed LiB cell SOC estimator. When tested on a parallel cell connected battery pack under US06 driving profile, the proposed battery pack SOC estimation technique addresses the problems associated with the current methods, i.e., accurate battery pack SOC estimation and proper representation of cells operating near extreme conditions. Thus, the proposed methods contribute to accurate LiB pack SOC estimation, leading to increased battery lifespan." @default.
• W4384306254 created "2023-07-15" @default.
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• W4384306254 date "2023-11-01" @default.
• W4384306254 modified "2023-10-05" @default.
• W4384306254 title "Electric vehicle battery pack state of charge estimation using parallel artificial neural networks" @default.
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• W4384306254 doi "https://doi.org/10.1016/j.est.2023.108333" @default.
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