# A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

#### Problem Statement

## 3. Methodology

#### 3.1. Process Overview

#### 3.2. Sensor Data Collection

#### 3.3. Preprocessing Using Z-Score Normalization

#### 3.4. Feature Extraction

#### Feature Extraction Using Sparse Principal Component Analysis

#### 3.5. Feature Selection Using Enhanced Marine Predators’ Algorithm

#### 3.6. Incipient Bat-Optimized Deep Residual Network

- Each bat employs the advantages of echolocation to look for prey and avoid obstacles.
- Each bat searches for food by flying at a velocity v
_{i}, location x_{i}, constant frequency fmin, variable wavelength, and loudness L_{0}. Depending on how close the target is, bats may automatically change the frequency of their produced pulses and the rate of pulse emission r in the range [0, 1]. - A minimal constant value L
_{min}and a large positive number L_{0}represent the range of loudness L_{m}.

## 4. Performance Analysis

## 5. Discussions

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Abbreviations | Definitions |
---|---|

EV | Electric Vehicles |

BEV | Battery Electric Vehicles |

BMS | Battery Management System |

SOC | State of Charge |

SOH | State of Health |

ECU | Vehicle or Electronic Controller Units |

OSMC-EV | Operation Service and Management Center for Electric Vehicle |

ML | Machine Learning |

FTA | Fault Tree Analysis |

IB-DRN | Incipient Bat optimized Deep Residual Network |

ICC | Interclass Correlation Coefficient |

SPCA | Sparse Principal Component Analysis |

PCA | Principle Component Analysis |

SVD | Singular Value Decomposition |

PCs | Principal Components |

QR decomposition | QR factorization | Q is orthonormal, and R is higher triangular |

SPCs | Sparse Principal Component |

PVE | Proportion of Explained Variation |

EMPA | Enhanced Marine Predators Algorithm |

ANN | Artificial Neural Network |

SVM | Support Vector Machine |

LR | Linear Regression |

GPR | Gaussian Process Regression |

UDDS | Urban Dynamometer Driving Schedule |

TP | True Positives |

FP | False Positives |

FN | False Negatives |

MSE | Mean Squared Error |

RMSE | Root Mean Square Error |

Battery Voltage (V) | Battery Current (A) | Battery Temperature (°C) | State of Charge (%) | State of Health (%) | Charge/Discharge Status | Charging Power (kW) | Charging Time (h) | Cell Voltage (V)—Cell 1 | Cell Voltage (V)—Cell 2 | Cell Voltage (V)—Cell 3 | Cell Voltage (V)—Cell 4 | Cell Voltage (V)—Cell 5 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

385 | 80 | 35 | 75 | 95 | Charging | 10 | 2 | 3.8 | 3.9 | 3.7 | 3.6 | 3.5 |

386 | 82 | 35.2 | 75.2 | 94.9 | Discharging | 10 | 2.01 | 3.9 | 3.8 | 3.6 | 3.7 | 3.4 |

387 | 85 | 35.4 | 75.4 | 94.8 | Charging | 10 | 2.02 | 3.7 | 3.9 | 3.5 | 3.6 | 3.3 |

388 | 88 | 35.6 | 75.6 | 94.6 | Discharging | 10 | 2.03 | 3.8 | 3.7 | 3.6 | 3.5 | 3.4 |

389 | 90 | 35.8 | 75.8 | 94.4 | Charging | 10 | 2.04 | 3.9 | 3.8 | 3.7 | 3.6 | 3.5 |

Algorithm: The Enhanced MPA |
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Initialize search agents or the prey population i = 1,⋯,N |

t = 1 |

while (t < Max T) |

Calculate the fitness and create the Elite matrix |

$ift\frac{2\ast Max-T}{3}$ |

Update prey using Equation (11) |

else if $\frac{Max-T}{3}<t<\frac{2\ast Max-T}{3}$ |

for $i=1:0\frac{N}{2}$ |

Update prey based on Equation (14) |

for $i=\frac{N}{2}:N$ |

Update prey based on Equation (19) |

else if $\left(t>\frac{2\ast Max-T}{3}\right)$ |

Update prey based on Equation (22) |

End if |

Achieve memory saving and update Elite matrix |

Apply the effect and update using Equation (24) |

Accomplish memory saving and update Elite matrix |

end while |

Algorithm: Incipient Bat Algorithm |
---|

1 Define the objective function $\left(x\right)$; |

2 Initialize the bat population ${x}_{i}\left(i=\mathrm{1,2},\dots ,n\right)$; |

3 for each bat ${x}_{i}$ in the population do |

4 Initialize the velocity ${v}_{i,}$ the pulse rate ${r}_{i}$ and loudness ${A}_{i}$; |

5 Define the frequency ${f}_{i}$ at position ${x}_{i}$; |

6 while termination criterion not reached do |

7 for each bat ${x}_{i}$ in the population do |

8 if rand $>{r}_{i}$ then |

9 Select one solution among the best ones. |

10 Generate local solution around the best one. |

11 Generate a new solution by flying randomly. |

12 if $rand<{A}_{i}and\psi \left({x}_{i}\right)\psi \left({x}_{*}\right)$ then |

13 Accept the new solutions. |

14 Increase the ${r}_{i}$ and reduce ${A}_{i}$; |

15 Rank bats and find current best solution ${x}_{*}$ |

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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kosuru, V.S.R.; Kavasseri Venkitaraman, A.
A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection. *World Electr. Veh. J.* **2023**, *14*, 101.
https://doi.org/10.3390/wevj14040101

**AMA Style**

Kosuru VSR, Kavasseri Venkitaraman A.
A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection. *World Electric Vehicle Journal*. 2023; 14(4):101.
https://doi.org/10.3390/wevj14040101

**Chicago/Turabian Style**

Kosuru, Venkata Satya Rahul, and Ashwin Kavasseri Venkitaraman.
2023. "A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection" *World Electric Vehicle Journal* 14, no. 4: 101.
https://doi.org/10.3390/wevj14040101