# An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Modeling and Description of the Entire System

#### 3.1. Proposed System’s Design Configuration

#### 3.2. Arrangement of Solar PV Array

_{G}is produced by solar irradiation, as shown below:

_{S}is explicit as the PV cell saturation current and temperature variation based on the following relationship:

#### 3.3. PV Characteristics

^{2}, 800 W/m

^{2}, 500 W/m

^{2}, 350 W/m

^{2}) are depicted in Figure 3, as well as the V-I and P-V characteristics at changing temperature and constant irradiation are depicted in Figure 4.

#### 3.4. DC-DC Boost Converter Equivalent Circuit

_{DUTY}= duty cycle, T′

_{RISE}= switch sw1 is in closed at the moment of raising the inductor current, T′

_{FALL}= switch sw1 is open at the moment when the inductor current is falling.

#### 3.5. BESS Equivalent Circuit

^{−1}), A is exponential voltage (V), and B is the exponential capacity (Ah

^{−1}).

_{b}(t) = terminal voltage, i(t) = terminal current, and V

_{ca}(t) = voltage across RC, which cannot be directly computed.

#### 3.6. Modeling and Motor Choice

## 4. Control Method Using MPA Technique

_{out}and V

_{in}are boost converter output and input voltages, and d denotes the duty cycle. This article gives a new bioinspired algorithm based on marine predators’ social behavior pattern.

#### 4.1. Marine Predator Algorithm

_{i,j}gives the jth position of the prey and is given by Equation (17).

#### Optimization Process of MPA

#### 4.2. Implementation of MPA for MPPT during PSCs

## 5. BLDC Motor Control Based on PID

#### PID Controlling

## 6. Results and Discussion

- Mode 1: Constant Motor Speed and Constant Irradiance

^{2}, as shown in Figure 12. Meanwhile the settling time is 0.06 s and 0.035 s for power through the GWO and WOA, respectively. This produces a high fluctuating signal. The output power through the MPA is settled in 0.02 s. In the first case, the PV panel temperature is 25 °C constant, and the PV irradiance is 1000 W/m

^{2}. In affixing, the BLDC motor speed is set at 3000 rpm constant, and the battery voltage and PV power outputs are analyzed in Figure 13. The PV current, PV voltage, and PV power are illustrated in Figure 13; the solar PV power reaches 62 kW and settles in 0.02 s, and the PV current and voltage are obtained at 185 A and 340 V, respectively. Figure 13 demonstrates the outputs of the battery, which are the current, SOC, and output voltage of the battery. The battery current and voltage are obtained at 480 A and 360 V, respectively, and battery SOC is reached at 100% in discharging mode.

## 7. BLDC Motor Outputs

- Mode 2: Constant motor speed and varying irradiation

^{2}irradiation, 0.5–1 s in 500 W/m

^{2}, with 3000 rpm motor speed and 25 °C constant temperature. In Figure 17, the power flow across the MPA is settled in 0.02 s. The power through the GWO and WOA is settled in 0.06 and 0.35 s, respectively. This produces a highly fluctuated signal. The suggested power from solar PV reaches 63 kW in 0–0.5 s at an irradiance of 1000 W/m

^{2}, as shown in the graph. Then, the power is changed to 40 kW after 0.5–1 s at an irradiance of 500 W/m

^{2}. The suggested approach is related to the traditional approach, such as the GWO and WOA MPPT algorithm, and proves the advancement of the suggested method. The battery and PV output is depicted in Figure 18. Figure 18a illustrates the PV power, voltage, and current attained at 62 kW, 340 V, and 185 A, respectively, that were generated in solar PV at 0–0.5 s time range. The irradiance is 1000 W/m

^{2}, and then suddenly PV power, PV current, and PV voltage raise to 61 kW, 175 A, and 325 V, respectively, and irradiance to 500 W/m

^{2}.

^{2}. After 0.5 s at the same motor speed, PV irradiance is set to 500 W/m

^{2}. The speed of the actual speed-to-reference speed comparisons is depicted in Figure 19b. The torque of the BLDC motor and stator current is shown in Figure 20; Figure 20a shows the high torque starting stage of the motor after 0.01 s, which dropped immediately to settle at 0.01 s on 1.2 Nm. The comparison of speed during the constant speed and variable irradiance of the motor is shown in Figure 20b. The Hall signal and back EMF of all phases are shown in Figure 21a,b.

- Mode 3: Variable motor speed and constant irradiation

^{2}for constant irradiance, 3000 W/m

^{2}for 0–0.2 s, 1000 W/m

^{2}for 0.2–0.4 s, 1500 W/m

^{2}for 0.6–0.8 s, 2500 W/m

^{2}for 0.6–0.8 s, and 2000 W/m

^{2}for 0.8–1 s for BLDC motor speed, respectively. Figure 22 illustrates the PV power at the variable motor speed and constant irradiance comparison. The suggested approach is more admirable than the GWO and WOA. According to the variation in the motor speed, the power will be varied. When the motor speed is 3000 rpm, the suggested technique power will reach 62 kW. When the motor speed is decreased to 1000 rpm, the suggested technique power is increased from 62 KW to reach a power of 81 kW. Accordingly, for motor speeds of 1500 rpm, 2000 rpm, and 2500 rpm, the power of the suggested approach will be 78 kW, 70 kW, and 68 kW, respectively. Figure 23 depicts the PV output and battery output at 1000 W/m

^{2}of constant irradiance, and variable speeds of 3000 between 0 and 0.2 s, 1000 from 0.4–0.6 s, 2500 from 0.6–0.8 s, and 2000 from 0.8–1 s are used. The PV current, power, and voltage are shown in Figure 23a. In that PV power for the regular interval of (0.2, 0.4, 0.6, 0.8), high changes may occur for the regular interval. In the meantime, the voltage of PV is drained to 325 V, and the current of PV is increased to 220 A. The battery SOC, battery current, and voltage of the battery are depicted in Figure 24. The changes in battery current and voltage, as well as battery charging and discharging, are caused by the variable motor-speed input. The BLDC motor Hall signal and back EMF at variable speed and constant irradiance are depicted in Figure 25.

- Mode 4: Variable Motor Speed and Variable Irradiance

^{2}for 0–0.5 s before shifting to 500 W/m

^{2}for 0.5–1 s. Additionally, the speed is fixed at 3000 rpm for 0–0.2 s, 1000 rpm for 0.4–0.6 s, 1500 rpm for 0.6–0.8 s 2500 rpm, and 2000 rpm for 0.8–1 s. The advanced approach is related to the traditional approach to prove its advantages. The PV and battery output are depicted in Figure 27. The output of the solar PV at variable speed and irradiance is illustrated in Figure 27a. Power from PV varies concerning varying irradiance and variable speed. Between 0 and 0.5 s, the variable irradiance is changed to 1000 W/m

^{2}and 500 W/m

^{2}between 0.5–1 s. Furthermore, the BLDC motor speed is modified, from 0 to 0.2 s, 3000 rpm, 1000 to 0.4 s, 1500 to 0.6 s, 2500 to 0.8 s, and 2000 to 1 s. All outcomes from PV are changed in favor of the variable speed and variable irradiance. Figure 27b shows the outputs of the battery current, battery voltage, and battery SoC. The BLDC motor speed and a comparison of its speed under different irradiation conditions are illustrated in Figure 28. The motor’s speed relative to the reference speed for 1000, 1500, 2000, 2500, and 3000 rpm is demonstrated in Figure 28a. The comparison speed to a reference speed is illustrated in Figure 28b. The reference speed is denoted as a straight red line, and the BLDC motor’s actual speed is mentioned as a blue line. The variable speed is given to 0, 0.2, 0.4, 0.6, and 0.8 s time intervals at 3000, 1000, 1500, 2500, and 2000 rpm. Figure 29 depicts the BLDC motor stator current and torque. Figure 29a illustrates the torque. Due to modifications made to the speed of the BLDC motor and irradiance of the solar PV panel, the peak and dip on the torque are now visible. Figure 29b illustrates all three-phase stator currents. Solar energy is used by the boost converter to power the 3000 rpm, 48 V, 1 kW BLDC motor and for battery charging. The operation of a variable speed BLDC motor is shown in Figure 28. We started the motor for 0.2 s and set the reference speed of 3000 rpm. After that, for 0.4 s it is shifted to 1000 rpm, then for 0.6 s it is changed to 1500 rpm, and at the end, it is changed to 2500 rpm. The speed of the motor reaches the designated reference speed in less than 0.01 s. Figure 29a shows the variation in the torque, first supplied at 100 Nm for 0.01 s. Due to speed changes, there are a few spikes in the torque. Figure 29b illustrates the motor current, the result of which is a spike representing a change in speed. Figure 30 illustrates the BLDC motor Hall signal and BLDC motor back EMF; it is varied with variation in speed of 1000, 1500, 2000, 2500, and 3000 rpm. According to the above results, the proposed approach is superior to the existing approach.

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Vertgewall, C.M.; Trageser, M.; Kurth, M.; Ulbig, A. Modeling Probabilistic Driving and Charging Profiles of Commercial Electric Vehicles. Electr. Power Syst. Res.
**2022**, 212, 108538. [Google Scholar] [CrossRef] - Sheela, A.; Logeswaran, T.; Revathi, S.; Rajalakshmi, K. Distributed MPPT Configuration for Improving Solar Energy Production. In Proceedings of the 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, 27–29 May 2022; pp. 1–5. [Google Scholar]
- Kee, S.H.; Chiongson, J.B.V.; Saludes, J.P.; Vigneswari, S.; Ramakrishna, S.; Bhubalan, K. Bioconversion of agro-industry sourced biowaste into biomaterials via microbial factories–A viable domain of circular economy. Environ. Pollut.
**2021**, 271, 116311. [Google Scholar] [CrossRef] [PubMed] - Sethi, V.; Sun, X.; Nalianda, D.; Rolt, A.; Holborn, P.; Wijesinghe, C.; Xisto, C.; Jonsson, I.; Gronstedt, T.; Ingram, J.; et al. Enabling Cryogenic Hydrogen-Based CO
_{2}-Free Air Transport: Meeting the demands of zero carbon aviation. IEEE Electrif. Mag.**2022**, 10, 69–81. [Google Scholar] [CrossRef] - Zengin, S. A hybrid current modulated DAB DC/DC converter for connecting PV modules to DC grid considering partial shading. Comput. Electr. Eng.
**2022**, 101, 108109. [Google Scholar] [CrossRef] - Yoon, Y.; Gi, H.; Lee, J.; Cho, M.; Im, C.; Lee, Y.; Bae, C.; Kim, S.J.; Lee, Y. A Continuously-Scalable-Conversion-Ratio Step-Up/Down SC Energy-Harvesting Interface With MPPT Enabled by Real-Time Power Monitoring With Frequency-Mapped Capacitor DAC. IEEE Trans. Circuits Syst. I Regul. Pap.
**2022**, 69, 1820–1831. [Google Scholar] [CrossRef] - Bansal, N.; Jaiswal, S.P.; Singh, G. Prolonged degradation and reliability assessment of installed modules operational for 10 years in 5 MW PV plant in hot semi-arid climate. Energy Sustain. Dev.
**2022**, 68, 373–389. [Google Scholar] [CrossRef] - Pandey, A.; Pattnaik, S. Design and Analysis of Extendable Switched-Inductor and Capacitor-Divider Network Based High-Boost DC-DC Converter for Solar PV Application. IEEE Access
**2022**, 10, 66992–67007. [Google Scholar] [CrossRef] - Wu, R.; Hao, J.; Zheng, S.; Sun, Q.; Wang, T.; Zhang, D.; Zhang, H.; Wang, Y.; Zhou, X. N dopants triggered new active sites and fast charge transfer in MoS2 nanosheets for full Response-Recovery NO2 detection at room temperature. Appl. Surf. Sci.
**2021**, 571, 151162. [Google Scholar] [CrossRef] - Li, Y.; Han, W.; Shao, W.; Zhao, D. Virtual sensing for dynamic industrial process based on localized linear dynamical system models with time-delay optimization. ISA Trans. 2022; in press. [Google Scholar] [CrossRef]
- Kim, M.-H.; Lee, S.-H.; Kim, S.; Park, B.-G. A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System. IEEE Access
**2022**, 10, 37030–37038. [Google Scholar] [CrossRef] - Zhang, W.; Xu, J.; Yu, T.X. Dynamic behaviors of bio-inspired structures: Design, mechanisms, and models. Eng. Struct.
**2022**, 265, 114490. [Google Scholar] [CrossRef] - Padmanaban, S.; Priyadarshi, N.; Bhaskar, M.S.; Holm-Nielsen, J.B.; Ramachandaramurthy, V.K.; Hossain, E. A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization. IEEE Access
**2019**, 7, 103377–103389. [Google Scholar] [CrossRef] - Lei, D.; Cui, Z.; Li, M. A dynamical artificial bee colony for vehicle routing problem with drones. Eng. Appl. Artif. Intell.
**2021**, 107, 104510. [Google Scholar] [CrossRef] - Rajeshkanna, G.; Sasiraja, R.M.; Winston, D.P. Design and development of Truncated Angle Variant (TAV) controller for multi-source-fed BLDC motor drive. Electr. Eng.
**2020**, 102, 1931–1946. [Google Scholar] [CrossRef] - Himabindu, N.; Hampannavar, S.; Deepa, B.; Swapna, M. Analysis of microgrid integrated Photovoltaic (PV) Powered Electric Vehicle Charging Stations (EVCS) under different solar irradiation conditions in India: A way towards sustainable development and growth. Energy Rep.
**2021**, 7, 8534–8547. [Google Scholar] - Lakshmiprabha, K.; Govindaraju, C. An integrated isolated inverter fed bldc motor for photovoltaic agric pumping systems. Microprocess. Microsyst.
**2020**, 79, 103276. [Google Scholar] [CrossRef] - Ahmad, F.; Khalid, M.; Panigrahi, B.K. An enhanced approach to optimally place the solar powered electric vehicle charging station in distribution network. J. Energy Storage
**2021**, 42, 103090. [Google Scholar] [CrossRef] - García, M.; Ponce, P.; Soriano, L.A.; Molina, A.; MacCleery, B.; Romero, D. Lifetime Improved in Power Electronics for BLDC Drives using Fuzzy Logic and PSO. IFAC-PapersOnLine
**2019**, 52, 2372–2377. [Google Scholar] [CrossRef] - Ho, J.C.; Huang, Y.-H.S. Evaluation of electric vehicle power technologies: Integration of technological performance and market preference. Clean. Responsible Consum.
**2022**, 5, 100063. [Google Scholar] [CrossRef] - Oubelaid, A.; Albalawi, F.; Rekioua, T.; Ghoneim, S.S.M.; Taib, N.; Abdelwahab, S.A.M. Intelligent Torque Allocation Based Coordinated Switching Strategy for Comfort Enhancement of Hybrid Electric Vehicles. IEEE Access
**2022**, 10, 58097–58115. [Google Scholar] [CrossRef] - Lan, H.; Hao, D.; Hao, W.; He, Y. Development and comparison of the test methods proposed in the Chinese test specifications for fuel cell electric vehicles. Energy Rep.
**2022**, 8, 565–579. [Google Scholar] [CrossRef] - Saha, B.; Singh, B. An Improved Flux Observer Based Position Sensorless Single Stage BLDC Motor Drive With Regenerative Braking For Solar Powered LEV. In Proceedings of the 2022 IEEE Transportation Electrification Conference & Expo (ITEC), Anaheim, CA, USA, 15–17 June 2022; pp. 1248–1253. [Google Scholar]
- Li, H.; Ning, X.; Li, W. Implementation of a MFAC based position sensorless drive for high speed BLDC motors with nonideal back EMF. ISA Trans.
**2017**, 67, 348–355. [Google Scholar] [CrossRef] [PubMed] - Gupte, S. Experimental Analysis and Feasibility Study of 1400 CC Diesel Engine Car Converted into Hybrid Electric Vehicle by Using BLDC Hub Motors. Energy Procedia
**2014**, 54, 177–184. [Google Scholar] [CrossRef][Green Version] - Faramarzi, A.; Heidarinejad, M.; Mirjalili, S.; Gandomi, A.H. Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Syst. Appl.
**2020**, 152, 113377. [Google Scholar] [CrossRef] - Soliman, M.A.; Hasanien, H.M.; Alkuhayli, A. Marine Predators Algorithm for Parameters Identification of Triple-Diode Photovoltaic Models. IEEE Access
**2020**, 8, 155832–155842. [Google Scholar] [CrossRef] - Vankadara, S.K.; Chatterjee, S.; Balachandran, P.K.; Mihet-Popa, L. Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions. Energies
**2022**, 15, 6172. [Google Scholar] [CrossRef] - Laxman, B.; Annamraju, A.; Srikanth, N.V. A grey wolf optimized fuzzy logic based MPPT for shaded solar photovoltaic systems in microgrids. Int. J. Hydrog. Energy
**2021**, 46, 10653–10665. [Google Scholar] [CrossRef] - Zafar, M.H.; Khan, N.M.; Mirza, A.F.; Mansoor, M.; Akhtar, N.; Qadir, M.U.; Khan, N.A.; Moosavi, S.K.R. A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustain. Energy Technol. Assess.
**2020**, 47, 101367. [Google Scholar]

**Figure 1.**Circuit arrangement for the advanced battery-operated, solar PV-powered, BLDC motor-operated system.

**Figure 13.**(

**a**) PV outputs at constant speed and irradiation (

**b**) battery outputs at constant speed and irradiation.

**Figure 19.**(

**a**) Speed of the BLDC motor (

**b**) comparison of motor speed and reference speed at varying irradiation.

**Figure 20.**(

**a**) BLDC motor torque with variable irradiation and constant speed (

**b**) BLDC motor stator current with variable irradiation and constant speed.

**Figure 21.**(

**a**) BLDC motor back EMF with variable irradiation and constant speed (

**b**) BLDC motor Hall signal with variable irradiation and constant speed.

**Figure 23.**(

**a**) PV outputs with varying speeds and constant irradiance (

**b**) battery outputs with varying speeds and constant irradiance.

**Figure 24.**(

**a**) BLDC motor torque at varying speeds and constant irradiation (

**b**) stator current at varying speeds and constant irradiation..

**Figure 25.**(

**a**) Back EMF of BLDC motor (

**b**) Hall signal of BLDC motor operating at variable speed and constant irradiation.

**Figure 27.**(

**a**) PV outputs with varying irradiance and varying speed (

**b**) battery outputs with varying irradiance and varying speed.

**Figure 28.**(

**a**) BLDC motor speed (

**b**) comparison of motor speed and reference speed with variable irradiance and variable speed.

**Figure 29.**(

**a**) BLDC motor torque with varying speed and irradiation (

**b**) stator current with varying speed and irradiation.

**Figure 30.**(

**a**) Back EMF for BLDC motor with varying speed and irradiation (

**b**) Hall signal for BLDC motor with varying speed and irradiation.

Methods | Tracking Time | Efficiency |
---|---|---|

P&O | 0.05 | 99.94 |

FLC | 0.05 | 99.96 |

AFLC | 0.038 | 99.97 |

Proposed MPA | 0.025 | 99.98 |

Criteria | P&O | FLC | ACO-FLC | Fuzzy-PSO | GWO-FLC | Proposed MPA |
---|---|---|---|---|---|---|

Tracking Speed | Slow | Moderate | Moderate | Moderate | Fast | Very Fast |

Complexity | Less | Less | Moderate | Moderate | Less | Very Less |

Tracking Efficiency | Less | Less | Medium | Medium | High | Very High |

Reliability | Low | Low | Low | High | High | Very High |

MPP Oscillations | High | High | Moderate | High | Less | Very Less |

Tracking accuracy | Medium | Medium | Medium | Medium | Accurate | High Accurate |

Methods | Convergence Time (s) | Settling Time (s) | Efficiency (%) |
---|---|---|---|

SRA | 0.1811 | 0.2402 | 99.98 |

GHO | 0.3112 | 0.5621 | 99.89 |

GWO | 0.4421 | 0.6514 | 99.88 |

PSOGS | 0.3522 | 0.6112 | 99.91 |

CS | 0.3801 | 0.7701 | 99.84 |

PSO | 0.4501 | 0.7102 | 99.86 |

Proposed MPA | 0.1532 | 0.2187 | 99.99 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 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**

Radhakrishnan, R.K.G.; Marimuthu, U.; Balachandran, P.K.; Shukry, A.M.M.; Senjyu, T.
An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems. *Sustainability* **2022**, *14*, 14120.
https://doi.org/10.3390/su142114120

**AMA Style**

Radhakrishnan RKG, Marimuthu U, Balachandran PK, Shukry AMM, Senjyu T.
An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems. *Sustainability*. 2022; 14(21):14120.
https://doi.org/10.3390/su142114120

**Chicago/Turabian Style**

Radhakrishnan, Rajesh Kanna Govindhan, Uthayakumar Marimuthu, Praveen Kumar Balachandran, Abdul Majid Mohd Shukry, and Tomonobu Senjyu.
2022. "An Intensified Marine Predator Algorithm (MPA) for Designing a Solar-Powered BLDC Motor Used in EV Systems" *Sustainability* 14, no. 21: 14120.
https://doi.org/10.3390/su142114120