Search

The accurate estimation of the state of charge (SOC) is crucial for the safe and reliable operation of Li-ion batteries. In this paper, novel dual-channel deep learning methods were proposed for SOC estimation during dynamic driving cycles. For dual-channel deep learning methods, each channel of the deep learning methods could be a single convolutional neural network (CNN) layer, single long short-term memory (LSTM) layer or sequentially combined structure of CNN and LSTM. After evaluation, it could be found that dual-channel deep learning methods could achieve higher accuracy than conventional single-channel method, however, the model size and floating-point operations (FLOPs) could be either reduced or increased. Among all the dual-channel deep learning methods, CNN + LSTM-CNN + LSTM method could achieve the highest accuracy and the shortest training time with sacrifices in computational complexity. CNN–CNN + LSTM method could achieve higher accuracy than the single-channel deep learning method with reduced computational complexity and model size. As the error of SOC estimation is highly correlated with voltage signals, conditional judgement was then integrated with dual-channel deep learning method to obtain higher accuracy. After integrated with conditional judgement, the averaged error of SOC estimation could be reduced by more than 45% without a substantial sacrifice in computational time. • Dual-channel deep learning methods are proposed for SOC estimation. • Two channels are responsible for processing of transient and moving averaged signals. • Model hyperparameters are optimized by Tree-structured Parzen estimator. • Cross validation method is applied to evaluate the proposed methods. • Conditional judgement is proposed to obtain higher accuracy. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of charge (SOC), state of energy (SOE), and state of health (SOH) online is a critical and urgent concern in the management of lithium-ion batteries for electric vehicle applications, particularly in terms of safety and reliability. This paper develops a hybrid neural network, abbreviated as CNN-SAM-LSTM model, which combines a convolutional neural network, self-attention mechanism, and long-short term memory neural network to jointly estimate the state parameters of lithium-ion batteries, including SOC, SOE, and SOH. Additionally, a joint loss function considering homoscedastic uncertainty is developed to optimize weight adjustments for the training losses associated with the three state parameters. Experimental data collected under UDDS, BBDST and CC discharge conditions are employed to showcase the effectiveness of the proposed CNN-SAM-LSTM model. The results demonstrate that the proposed model is capable of simultaneously and accurately estimating SOC, SOE, and SOH for lithium-ion batteries under different dynamical operating conditions. Moreover, when applied to randomly segmented data, the proposed model exhibits robustness, effectively handling deviations from random discharge segments. • A CNN-SAM-LSTM hybrid neural network is developed to estimate SOC, SOE and SOH. • The loss function of the hybrid network considers homoscedastic uncertainty. • The proposed method achieves high accuracy under dynamic operating conditions. • The proposed method is strongly robust against random discharge segments. [ABSTRACT FROM AUTHOR]

Capturing the lifespan trajectory of lithium-ion (Li-ion) batteries in the early stage is critical for the operation and maintenance of battery energy storage systems (BESSs). Recently, data driven model is a promising solution to implement this task, yet the battery's early cycling stage can only provide very limited information in the training phase and the scenarios of the BESS applications are not immutable. To alleviate the above issues, this paper proposes a multi-scenario transferable learning framework with few-shot to predict the Li-ion battery lifespan trajectory. An easily constructed model is chosen to generate various pseudo trajectories from only the full lifespan trajectory of a single cell. Then, a transferred deep learning method integrating the gate recurrent unit (GRU) and one-dimensional convolutional neural network (1D CNN) is proposed to utilize the pseudo curves, whose training ability can be adjusted to a new scenario using simply the first 100 cycling data and a pre-trained strategy. Finally, the proposed framework can accurately predict the whole Li-ion battery aging trajectory for multi-scenarios in two different datasets. • A multi-scenario transferable learning framework is proposed to predict the lifespan trajectory of lithium-ion battery. • Various pseudo trajectories are generated by an easily constructed model from only one cell. • A transferred deep learning method is proposed to utilize the pseudo curves. • The proposed framework can accurately predict the battery aging trajectory in two different datasets. [ABSTRACT FROM AUTHOR]