Table 1 Recent prediction models for lithium battery.
From: A conditional random field based feature learning framework for battery capacity prediction
Authors | Year | Approach |
|---|---|---|
Zhang et al.31 | 2016 | Relevance vector machine |
Wang et al.37 | 2017 | State space model |
Gao et al.26 | 2017 | Multi-kernel support vector machine with particle swarm optimization |
Zhang et al.38 | 2018 | Particle filter and unscented Kalman filter |
Zhang et al.25 | 2018 | LTSM |
Ren et al.41 | 2018 | Autoencoder with deep neural network |
Fang et al.36 | 2019 | Double extended Kalman filter |
Deng et al.43 | 2019 | Least squares support vector machine |
Fan et al.32 | 2020 | GRU-CNN |
Zhou et al. 33 | 2020 | Temporal convolutional network |
Song et al.39 | 2020 | Principal component analysis and support vector machine |
Ren et al.40 | 2020 | CNN-LSTM |
Kodjo S.R.Mawonou et al.42 | 2021 | Random forest |
Hong et al.44 | 2021 | Locally linear embedding and isomap |
Jungsoo Kim et al.45 | 2022 | Genetic algorithm and pseudo-2-dimensional model |
