# Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Battery State of Charge Prediction Based on Extreme Learning Machine Algorithm

#### 3.1. Learning Algorithm for Regularized Extreme Learning Machine Based on Alternating Direction Multiplier Method

#### 3.2. Recursive ADMM-Based Sparse Simple Learning Model for State of Charge Prediction

^{−9}, wherein the number of hidden nodes L is set to 50; The value of iteration number k is 1000; F(t) is the new hidden layer output of input data under the number of t; and Q(t) is the Convex function solution function under the Identity matrix I. The functional expressions for F(t) and Q(t) are given in Equation (21).

## 4. ADMM-ETLM Performance and Results Analysis of Sparse Neural Networks in Lithium Battery SOC Prediction

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

Variable | Abbreviation |

Machine Learning | ML |

Extreme Learning Machine | ETLM |

State of Charge | SOC |

Li-ion Battery State of Charge | LBSC |

Feedforward Neuron Network | FNN |

Convex Function | CF |

Root Mean Square Error | RMSE |

Ridge Regression | RR |

Alternate Direction Method of Multipliers | ADMM |

Predicted Output Value | POV |

Hidden Layer | HL |

Sparse Neural Network | SNN |

Optimally Pruned Extreme Learning Machine | OP-ETLM |

Average Error | AE |

Long Short-Term Memory | LSTM |

Gated Recurrent Neural | GRN |

Command Query Second Order Cone Programming | CQSOCP |

Generalized Outlier Robustness-Extreme Learning Machine | GOR-ETLM |

University of California Irvine | UCI |

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**Figure 6.**Testing the relationship between the root mean square error, the implied layer nodes, and the regularization penalty factor.

Name | Feature Data | Number of Training Samples | Test Data Sample Size |
---|---|---|---|

Wine quality white | 11 | 3000 | 1898 |

Parkinsons Telemonitoring | 22 | 2875 | 3000 |

Abalone | 8 | 2784 | 1393 |

Servo | 4 | 110 | 57 |

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**MDPI and ACS Style**

Zhang, B.; Ren, G.
Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine. *World Electr. Veh. J.* **2023**, *14*, 202.
https://doi.org/10.3390/wevj14080202

**AMA Style**

Zhang B, Ren G.
Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine. *World Electric Vehicle Journal*. 2023; 14(8):202.
https://doi.org/10.3390/wevj14080202

**Chicago/Turabian Style**

Zhang, Baozhong, and Guoqiang Ren.
2023. "Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine" *World Electric Vehicle Journal* 14, no. 8: 202.
https://doi.org/10.3390/wevj14080202