Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction

Abstract

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State of Power Prediction for Lithium-Ion Batteries in Electric Vehicles via Wavelet-Markov Load Analysis

“…The obtained model shows a root mean square error (RMSE) of 1.85 mV and the maximum error of 221.39 mV at the end of the discharge range when the battery shows more non-linear behaviour. Further details on optimising the battery ECM model is given in [45].…”

“…On the other hand, too few clusters may result in a model unable to represent the whole data properly. Discussions on the effect of the cluster number on the quality of clustering procedure can be found in [45]. In current study the number of cluster is set to 2.…”

Development and Real-Time Performance Evaluation of Energy Management Strategy for a Dynamic Positioning Hybrid Electric Marine Vessel

“…Amongst them, lithium-ion batteries are the most competitive candidate because of their unique features such as their high energy density, long life cycle, high efficiency, and environmental-friendly performance [29]. Recent studies also show that advanced lithium-ion battery models such as the equivalent circuit model (ECM) [30,31] and electrochemical-based battery models [32,33] have been employed in the control design and energy management systems. However, a drawback that remains in such studies is the calculation effort, which has a negative impact on the control performance, especially for real-time feedback control and verification.…”

A novel streamlined particle‐unscented Kalman filtering method for the available energy prediction of lithium‐ion batteries considering the time‐varying temperature‐current influence

“…In the proposed equivalent model, the discharge direction is positive, and the voltage across the polarization resistance increases along with the current rising process. Subsequently, the special time-domain relationship can be obtained as a supplement to the iterative calculation as shown in Equation (2). Then, the probability distribution characteristics of these specific data points are similar to the known variables.…”

“…The average circulating current has a great influence on the available energy status of lithiumion batteries, which can be predicted by using Dual Extended Kalman Filtering, Gaussian Markov Modeling, and Unscented Particle Filtering algorithms. [1][2][3] The relaxation behavior of exotic lithium-ion batteries can be extracted to express its capacity decaying characteristics, including Dynamic Linear Modeling, Long Short-Term Memory (LSTM), Neural Network (NN), and Wide Operating Temperature Degradation methods. [4][5][6][7][8] Consequently, the robust adaptive Sliding Mode Observation algorithm is introduced into the effective and predictable correction stage as well as its diagnosis evaluation in advance.…”