Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
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Summary
This review categorises data-driven battery health estimation methods according to their underlying models/algorithms and discusses their advantages and limitations, then focuses on challenges of real-time battery health management and discuss potential next-generation techniques.
- Type
- review
- Published
- 2019-10-01
- Cited by
- 1,027
- References
- 147
- Access
- Open access
- OpenAlex
- https://openalex.org/W2957056027
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:199085523
Keywords
Estimation, Lithium (medication), Ion, Computer science, Reliability engineering
References
- Bayesian Filtering and Smoothing
- Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model
- Probabilistic machine learning and artificial intelligence
- A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation
- Synthesize battery degradation modes via a diagnostic and prognostic model
- Cycle ageing analysis of a LiFePO4/graphite cell with dynamic model validations: Towards realistic lifetime predictions
- Differential thermal voltammetry for tracking of degradation in lithium-ion batteries
- Accelerated lifetime testing methodology for lifetime estimation of Lithium-ion batteries used in augmented wind power plants
- Differential voltage analyses of high-power lithium-ion cells: 3. Another anode phenomenon
- Method for estimating capacity and predicting remaining useful life of lithium-ion battery
- Identify capacity fading mechanism in a commercial LiFePO4 cell
- A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries
- Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery
- Overcharge reaction of lithium-ion batteries
- Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries
- Modeling and simulation of lithium-ion batteries
- Modeling, validation and analysis of mechanical stress generation and dimension changes of a pouch type high power Li-ion battery
- Capacity and power fading mechanism identification from a commercial cell evaluation
- A review on prognostics and health monitoring of Li-ion battery
- Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries
Cited by
- State estimation for advanced battery management: Key challenges and future trends
- Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries
- A review on prognostics and health management (PHM) methods of lithium-ion batteries
- A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation
- Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries
- Capacity Estimation of Serial Lithium-ion Battery Pack Using Dynamic Time Warping Algorithm
- Optimal Hierarchical Management of Shipboard Multibattery Energy Storage System Using a Data-Driven Degradation Model
- Battery Safety: Data-Driven Prediction of Failure
- MSDF-Net: Multi-Scale Deep Fusion Network for Stroke Lesion Segmentation
- Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine
- Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles
- SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators
- Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols
- Battery Lifetime Prognostics
- Control-Oriented Modelling of Solid-Electrolyte Interphase Layer Growth for Li-Ion Batteries
- Artificial intelligence and machine learning for targeted energy storage solutions
- Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features
- Predicting the state of charge and health of batteries using data-driven machine learning
- A review of the state of health for lithium-ion batteries: Research status and suggestions
- State of Health Monitoring and Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Temporal Convolutional Network
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