# Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery

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## Abstract

**:**

## 1. Introduction

- (a)
- Proposed the NNPC–LSTM controller that combined the advantages of the NN and MPC controllers and was supported by the LSTM model for fast-charging the lithium-ion batteries.
- (b)
- Utilized the LSTM network model in the offline forecasting of the PV output power, which was fed to the NNPC as training data. In addition, the LSTM was a flagger to the charging process if the PV output power was not sufficient for implementing the MSCC protocol.
- (c)
- Investigated the system dynamic behavior during the charging process under various circumstances, while presenting the proposed NNPC-LSTM with respect to the FL controller and the conventional PID controller based on the MSCC protocol as a complement to our research in [20];
- (d)
- Emphasized the superiority of the proposed controller during the lithium-ion battery charging process by an experimental implementation that was in good agreement with the simulated results.

## 2. The Controllable Fast-Charging System Understudy

#### 2.1. Parasitic Buck Converter Model

#### 2.2. Charging System Understudy

#### 2.3. Conventional and Proposed Controllers of the DC–DC Buck Converter

#### 2.3.1. PID Controller

#### 2.3.2. Fuzzy Logic Controller (FLC)

#### 2.3.3. Neural Network Predictive Controller (NNPC)

#### Long-Short Term Memory (LSTM) Model

## 3. Theoretical and Experimental Analysis of the PID, FLC, and NNPC–LSTM Controllers

#### 3.1. Simulated Results

#### 3.2. Experimental Validation

#### 3.2.1. PV Output Power Based on the Solar Climate and Module Characteristics

#### 3.2.2. Experimental Analysis

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) The proposed construction of the charging control system. (

**b**) The ohmic internal resistance (Ri). (

**c**) The electrochemical polarization internal resistance (Rα). (

**d**) The concentration polarization internal resistance (Rβ). (

**e**) The electrochemical polarization capacitance (Cα). (

**f**) The concentration polarization capacitance (Cβ). (

**g**) Graphical s-plane model of the DC–DC buck converter.

**Figure 3.**Graphical illustrative schematic of the (

**a**) the Proportional Integral Derivative controller (PID), (

**b**) Fuzzy logic controller (FLC), and (

**c**) the neural network predictive controller (NNPC).

**Figure 6.**Simulated results for NNPC-LSTM and PID controllers where (

**a**) reference voltage changed from 7.7 V, 5.6 V, and 8 V, and (

**b**) an input voltage changed from 25 V to 12 V.

**Figure 7.**(

**a**) The daily average amount of the total solar radiation incident to the horizontal surface at the surface at El Shorouk, Cairo, Egypt; (

**b**) the PV output power readings for 34 min; (

**c**) the relation between the PV output voltage and the current of the solar panel understudy; and (

**d**) predicted and measured PV output voltage from the LSTM method.

**Figure 8.**(

**a**) Experimental results of the NNPC–LSTM, FL, and PID controllers with the reference voltage changes of 7.7 V, 5.6 V, and 8 V, respectively. (

**b**) Experimental results for the NNPC–LSTM, FL, and PID controllers, with an input voltage change from 25 V to 12 V. (

**c**) The dynamic behavior of the lithium-ion battery during the charging process.

**Table 1.**Comparison among various controllers from the literature, including the proposed controller.

Type of Controller | Steady-State Error (V) | Peak Overshoot (V) | Output Ripple Voltage (V) | Settling Time (ms) | Input Voltage (V) | Load |
---|---|---|---|---|---|---|

MNSGA-II based PID [53] | 0 | 0 | 0.06 | 1.34 | Variable 25 V–18 V | Resistive |

NSGA-II based PID [53] | 1.2 | 5 | 0.8 | 5.32 | Variable 25 V–18 V | Resistive |

Offset-free model predictive controller [35] | 0 | 2 | NA | 2 | Variable 200 V–400 V | Resistive |

Model predictive controller [54] | NA | 0 | NA | 1.4 | Variable 26.04 V–30.38 V | Battery |

Second-order sliding mode [55] | NA | NA | 0.1 | ~10 | Variable 30 V–20 V | Resistive |

Sliding mode-based control [56] | 0 | 0.1 | NA | 0.15 | Constant 10 V | Resistive |

Artificial neural network (ANN)-based approximate dynamic programming (ADP) [3] | 0 | 2 | NA | 3 | Variable 42 V–47 V | Resistive |

PSO-optimized fuzzy PI controller [57] | NA | NA | 2.5 | ~5 | Constant 24 V | PMSM motor |

Tuned fuzzy logic controller (TFLC) [58] | 0.01 | 0 | NA | 7 | Constant 15 V | Resistive |

Fractional-order PID controller [59] | 0 | 0.6 | NA | 0.02 | Constant 100 V | Resistive |

Proposed controller (NNPC–LSTM) | 0 | 0 | 0.001 | 1 | Random variation 25 V–12 V | Polymer lithium-ion battery |

Error (E)/Change in Error (CE) | NB | NS | ZE | PS | PB |
---|---|---|---|---|---|

NB | PB | PB | PS | PS | PS |

NS | PB | PS | PS | PS | ZE |

ZE | PS | PS | ZE | NS | NS |

PS | ZE | NS | NS | NS | NB |

PB | NS | NS | NS | NB | NB |

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**MDPI and ACS Style**

Makeen, P.; Ghali, H.A.; Memon, S.
Theoretical and Experimental Analysis of a New Intelligent
Charging Controller for Off-Board Electric Vehicles Using
PV Standalone System Represented by a Small-Scale
Lithium-Ion Battery. *Sustainability* **2022**, *14*, 7396.
https://doi.org/10.3390/su14127396

**AMA Style**

Makeen P, Ghali HA, Memon S.
Theoretical and Experimental Analysis of a New Intelligent
Charging Controller for Off-Board Electric Vehicles Using
PV Standalone System Represented by a Small-Scale
Lithium-Ion Battery. *Sustainability*. 2022; 14(12):7396.
https://doi.org/10.3390/su14127396

**Chicago/Turabian Style**

Makeen, Peter, Hani A. Ghali, and Saim Memon.
2022. "Theoretical and Experimental Analysis of a New Intelligent
Charging Controller for Off-Board Electric Vehicles Using
PV Standalone System Represented by a Small-Scale
Lithium-Ion Battery" *Sustainability* 14, no. 12: 7396.
https://doi.org/10.3390/su14127396