In this paper, investigation on the effect of separator thickness and porosity on the performance of Lithium Iron Phosphate batteries are analyzed. In recent years there have been intensive efforts to improve the performance of the lithium-ion batteries. Separators are important component of lithium-ion batteries since they isolate the electrodes and prevent electrical short-circuits. Separators are also used as an electrolyte reservoir which is used as a medium for ions transfer during charge and discharge. Electrochemical performance of the batteries is highly dependent on the material, structure, and separators used. This paper compares the effects of material properties and the porosity of the separator on the performance of lithium-ion batteries. Four different separators, polypropylene (PP) monolayer and polypropylene/polyethylene/polypropylene (PP/PE/PP) trilayer, with the thickness of 20 μm and 25 μm and porosities of 41%, 45%, 48%, and 50% were used for testing. It was found that PP separator with porosity of 41% and PP/PE/PP separator of 45% porosity perform better compared to other separators.

Energy storage technologies are being used excessively in industrial applications and in automobiles. Battery state of charge (SOC) is an important metric to be monitored in these applications to ensure proper and safe functionality. Since SOC cannot be measured directly, this paper puts forth a novel machine learning architecture to improve on the existing methods of SOC estimation. This method consists of using combined stacked bi-directional LSTM and encoder–decoder bi-directional long short-term memory architecture. This architecture henceforth represented as SED is implemented to overcome the nonparallel functionality observed in traditional RNN algorithms. Estimations were made utilizing different open-source datasets such as urban dynamometer driving schedule (UDDS), highway fuel efficiency test (HWFET), LA92 and US06. The least Mean Absolute Error observed was 0.62% at 25 °C for the HWFET condition, which confirms the good functionality of the proposed architecture.

Conventional model-based probabilistic inference methods require increasingly complex models to improve Electric Vehicle (EV) battery State of Charge (SOC) estimation. Deep learning methods gained popularity in recent years with their model free estimations. However, practical constraints such as insufficient training data, model complexity for real time implementation, and generalization on new dataset hinder performance reliability. Another major practical drawback of the data driven deep learning approach is its poor convergence from an unfamiliar initial error state as training dataset does not adequately accommodate these practical error scenarios. This paper proposes an ensemble method that uses a weighted estimate of the Central Difference Kalman Filter (CDKF) and Nonlinear Autoregressive with Exogenous Input (NARX) to accurately estimate SOC in the early stages of degradation. We employ a parallel ensemble estimation method that reduces the estimation bias, improves generalization, accuracy, robustness, and reliability of the estimator. We propose a pre-estimated voting weight to combine the ensemble algorithm and employ the CDKF covariance dependent method as the optimum approach for initializing the ensemble system to achieve a robust convergence performance. The state converges on an average of 136 time-steps when initialized halfway from the true state. The average Mean Absolute Error (MAE) performance of the ensemble method is about 0.5 % with an average training data of about 31,041 time-steps. The model was validated using conventional drive cycle data and was shown to outperform its individual ensemble members and gated Recurrent Neural Networks (RNN) such as Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and the Bidirectional LSTM (BiLSTM).