Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling

By Xiaosong Hu, Jiuchun Jiang, Dongpu Cao, Bo Egardt. IEEE transactions on industrial electronics (1982. Print), 2016

460 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b3c0acf284175d9af38b114e633692cf6df3ddc6

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In Automotive Engineering

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Citations in the corpus

Gaussian process regression for forecasting battery state of health (2017, background): “For instance, a number of recent studies have used Support Vector Machines (SVMs) for predicting future cell capacity based on it’s historical capacity vs. cycle data and/or from data from multiple identical cells [11, 12, 13, 14].”
Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system (2016, background): “Accurate SOH estimation is a key challenge for battery management systems (BMSs) [12].”
Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, background): “[8], [9], these processes and algorithms for SOH and/or RUL can be principally classified into four categories: 1) direct measurement; 2) battery model-based parameters identification closed-loop estimation; 3) durability model based open-loop estimation; and 4) data-driven method.”
Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries (2020, methodology): “vector regression [15], [16], Bayesian prediction [17], [18], and artificial neural network [19]–[21] have been successfully applied to build data-driven models for battery cyclic aging”
Active Disturbance Rejection Adaptive Control of Hydraulic Servo Systems (2017, background): “HYDRAULIC servo systems play a significant role in industrial applications, such as manipulators [1], [2], various vehicle components/subsystems [3]–[6], by virtue of their large power-to-weight ratios, high-response and high-load capability.”
State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries (2020, methodology): “Commonly used HIs include geometric or statistical features, such as the distance of two specific points in [20] or sample entropy in [12].”
Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “The Coulomb-counting method obtains the SOH by integrating the current of the full charge–discharge process, which is time consuming [3].”
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “Sample entropy (SE) can be used to evaluate the predictability of the time series and to quantify the regularity of data series [62].”
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “Data-driven methods for health estimation and prediction are gaining increasing interest in both academia and industry due to their advantages of flexibility and being model-free [17].”