Today, ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency, support high-power missile systems and reduce emissions. However, the electric propulsion of the shipboard system experiences torque fluctuation, thrust, and power due to the rotation of the propeller shaft and the motion of waves. In order to meet these challenges, a new solution is needed. This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system. The battery type and ultracapacitor are ZEBRA and MAXWELL, respectively. The 3-, 4-and 5-blade propellers are considered to produce power and move rapidly. The loss factor has been reduced, and the sea states have been found through the Elephant Herding Optimization algorithm. The efficiency of the proposed system is greatly enhanced through torque, thrust and power. The model predictive controller control strategy is activated to reduce load torque and drive system Root Average Square (RMS) error. The implementations are conducted under the MATLAB platform. The values for torque, current, power, and error are measured and plotted. Finally, the performance of the proposed methodology is compared with other available algorithms such as BAT and Dragonfly (DF). The simulation results show that the results of the proposed method are superior to those of various techniques and algorithms such as BAT and Dragonfly.

Electric propulsion and power generation schemes can make the vulnerable solution for the environmental impacts and provides satisfactory results in terms of fuel economy, increased availability, better comfort on board, and high flexibility. This configuration makes fault tolerance, and packages of propulsion system are generators, speed drivers (Fixed or variable), prime movers, electrical distributors, and motors are required for thrusters, and electrical system is required at low and high voltages [

♦ Ship speed dynamics.

♦ Shaft dynamics.

♦ Propeller characteristics.

♦ Diesel dynamics.

♦ Pitch and speed control of propellers.

Major faults generally appear in the propulsion system due to its significant loss. The terms force, velocity, position, and current have to be controlled, among which the force is governed by the force distribution function (FDF). Energy management is the primary criterion in the propulsion system. This is why a specific type of energy storage system (ESS) is being developed. The ESS of the battery and the capacitors are also derived to solve the problem. The battery and the supercapacitor have various types to manage the power and make the long life possible [

After using the only energy storage system, hybrid energy storage systems are developed to maintain power in the propulsion system. Although battery types such as nickel-zinc and cadmium allow for a high cost and discharge rate. Nickel batteries are used for developing a hybrid propulsion system [

An improved method of controlling average power has been proposed for power flow to supercapacitors to reduce disruptions and increase energy efficiency [

The battery is the major source of power for management systems. This makes the system much more efficient. Propulsion features include thrust, position, and torque. Various requirements are satisfied through the multiple loads and power sources. Speed and efficiency are significantly enhanced by the optimization approach in energy management systems [

The motors are operated from zero to maximum speed in an integrated power system at both conditions (forward and reverse). The dynamic ship with the propeller is designed, in which the energy is managed by the storage system of ultra-capacitors and Zebra batteries. The loss factors have been induced, and it is corrected using optimization algorithms [

Propulsion system energy management systems are derived from maintaining power and system efficiency through elements such as torque, speed, position and thrust. The layout of the paper is presented here. The literature survey is given in Section 2; the proposed methodology is given in Section 3, which includes mathematical modeling, algorithm; the results obtained from the MATLAB are added in Section 4; Section 5 makes the explanation about the results; and finally, the conclusion is presented in Section 6.

Some of this related work is covered below.

Torque and power fluctuations had been induced in the propulsion system as a result of their waves and rotational motions that had been integrated into the energy storage system. The physical behavior was designed to develop and optimize the controller. The power generation schemes enable the combination of the ultra-capacitor, and the battery was provided. The device coordination and effective power management were highly efficient due to the hybrid energy storage system developed by Hou et al. [

Energy storage systems (ESS) could continuously penetrate the electric vehicle and power grid and were established by Lashway et al. [

The efficiency of diesel generators was affected by heavy electrical loads and was established by Chen et al. [

An improved version of Ant Colony Optimization (ACO) with an optimized version of Proportional Integral and Derivative (PID) controller for load frequency control was developed by Chen et al. [

The challenge in that work was to be a voltage rising and frequency. The 17-level modular multilevel converter with 3.9 MW, 4.16 kV machine had been used. The main aim of the electric propulsion system was to reduce fuel consumption, and it increased the system's reliability. The power plant could generate electrical power due to the prime movers. The converter topology had been developed, in which the AC drives preferred instead of DC drives. The efficiency had been improved by adding the shaft at 360^{0} that was laid down by Hansen et al. [

The following are the contributions:

To design and develop a dynamic ship with either 3, 4, or 5 blade propellers.

To design the configuration of the energy storage system with ultra-capacitors and Zebra batteries.

To make use of the EHO algorithm for loss factor optimization during energy management.

To track maximum power and reduce RMS power with the help of Model Predictive Control (MPC).

To check the efficiency of the proposed concept using the MATLAB platform and compare the results with previously developed related works.

Within the marine engineering community, the behavior of electrical propulsion systems makes the propulsion system reliable. Inherent elements such as torque, fluctuations, energy, and thrust are identified as the main issue with the large surface ship. These elements appear due to wave excitation and the hydrodynamic interaction. In an electrical system, reduced efficiency, degraded electricity quality, and energy use are caused by power fluctuations.

The schematic diagram of the electric propulsion system is shown in

The dynamics of the motor and propeller of the ship are mechanically coupled and influence each other, as shown in

The propeller is said to be a fan, and it is the ship's forward movement due to the wind speed. A variety of propeller types are available. Below are three blades, four blades, and five propellers depicted in

The propeller characteristics are torque and thrust. The thrust coefficient (_{0}

T_{H} and T_{O} are computed by

The respective loss factor is added in

The loss factor lies under some constraints as given in

The shaft submergence is (

The mechanical power (_{pro}

The propeller speed shall be constant when power and torque change linearly with a large amplitude. If the torque is constant, speed changes occur. The power fluctuation is attenuated by the relevant hybrid energy storage system (HESS) and develop the control strategy to manage the power.

The fluctuation component of the wakefield is represented by

The angular position of the single blade is noted with (

The strength constraint (sc) is represented by

The strength constraints are computed by using pitch ratio (

The thrust constraint is represented by _{t} is the total ship resistance is to be and

The ship dynamics are associated with some wave excitation, wind, forcing function, etc.

The total resistance of the ship is computed by _{f} is the frictional resistance, R_{ewind} is the wind resistance, and R_{wm} is the wave-making resistance.

The frictional resistance is expressed by

The wind resistance is expressed by

The wave-making resistance is expressed by

The ZEBRA battery and the Maxwell ultra-capacitor store the power from the generation side for ship propulsion. The benefit of using the ZEBRA battery is high energy density, and the Maxwell ultracapacitor absorbs high current. Thus, HESS is used in this work to absorb high current and high energy density for ship propulsion. Initially, the power of the generation side is stored in the Maxwell ultra-capacitor as it has a long service life and high reliability. Once the power is ultimately stored in the Maxwell ultra-capacitor, then the power is stored in the ZEBRA battery. Both can charge and discharge. Firstly, the battery discharges the HESS power supply.

In this work, 550 V-38 Ah ZEBRA batteries are connected across the converters, and the specification of this battery is mentioned in

It is defined as the ratio of current capacity to the maximum capacity of the battery as given by

The power is expressed by _{bat} is battery power states that the number of batteries, V_{ockt} is the open-circuit voltage of the battery, I_{t} is the current of the battery, R_{abat} internal resistance of the battery.

The Maxwell ultra-capacitor with 125 V and 63 F capacitor is considered here, and the related mathematical expression is given by

The ultra-capacitor is defined with the terms of Number of ultra-capacitor (N_{UC}), Maximum voltage (V_{MAX}), Current (I_{UC}), the state value of ultra-capacitor (x_{UC}), power (P_{UC}), and internal resistance (R_{UC}).

The model predictive adaptive control strategy is used here to track the maximum power and reduce the error value presented in the system. Here the load torque is the plant model, and it is given by

The tracking error is said to be the RMS error, and it is computed by

The above procedures are followed in modeling the dynamic model of the ship with the propeller, thrust, and torque. This can be achieved through mathematical representation. Fuel consumption is reduced by a ship's electrical propulsion system. A predictive controller (MPC) controller is developed to track power and reduce the RMS error rate. The loss factor under the three states is estimated by the Elephant Herding Optimization algorithm (EHO), and the flow chart of the proposed modeling is shown in

A system model is developed and presented herein as a suitable numerical platform to support the analytical work and numerical investigation on the control and optimization of electric ship propulsion systems. This model includes diesel-generator sets and the associated diode rectifier as the primary power source, the hybrid energy storage system (HESS) with batteries, and ultra-capacitors (UC) as the energy storage. A detailed induction motor model and a propeller-ship dynamic model capture the fluctuations in the ship electric drive system induced by the rotating motions of the shaft and the waves and the DC bus dynamic model. Power electronic converters of the energy storage systems and the propulsion motor are used for the power flow control.

One kind of largest animal in the world is to be an elephant. It takes in two recognized species. The primary identification of the animal is a long trunk, which is for grasping, breathing, and lifting water. Several clans are oriented toward the leadership of the matriarch. Under the family, there is one female, calves. The female groups are recognized to live together even though the male elephants will live away from their family.

The EHO algorithm has three idealized functions which are,

The population of the elephant is composed of a kind of clan. The clans contain the fixed integer of elephants.

At each iteration, the male elephants will be separated from their family and stay solitary away with their groups.

Eventually, all the clans live together, which is called a matriarch.

This algorithm has the following two operators.

Separating operator

Clan operator

In each clan, all the elephants live together under the leadership. The clan of each elephant is said to be ^{th}” elephant in the clans by

The newly updated and the old positions are

The dimension of the clan is expressed by

The worst fitness is carried out with the separate operator, and it is processed for each iteration, which has been obtained by

Initially, the generation counter is set to 1. The loss factor, radius, and propeller shaft submergence are initialized to the maximum number of iterations. Mention the objective functions as given in

The results obtained from the implementation environment for different states of sea level are shown in

The energy management problem and control strategy are solved through the proposed approach, and the modeling is done in the working environment of MATLAB R2016a. The electric propulsion system of the dynamic model is presented in the above section to avoid the fuel consumption of the system and the cost reduction.

The torque, thrust, and power values are analyzed in the results section through the modeling of the propeller and the ship dynamics. The sea states are represented in

The wakefield of the propeller is shown in

It is quickly converged with up to 3000 iterations. The design values of the propeller used in work are mentioned in

Parameters | Values |
---|---|

0.1809 | |

0.0362 | |

10.57 | |

997 | |

0.25 | |

_{a} |
2.25 |

_{f} |
0.0043 |

_{a} |
12297 |

_{wind} |
0.8 |

_{wm} |
0.0043 |

Capacity (Ah) | 38 |
---|---|

Discharging current (A) | 1.12 |

Discharging power (W) | 2.128 |

End of the resistance (m-ohm) | 180 |

Maximum charging current (A) | 10 |

Maximum discharging current (A) | 1.17 |

Nominal energy (kWh) | 21.2 |

Number of cells | 216 |

Open circuit voltage (V) | 557 |

Power density (Wh/I) | 273 |

Specific power (W/kg) | 179 |

Weigh (kg) | 184 |

Parameters | Values |
---|---|

18 | |

557 | |

240 mA/117 A | |

2.5 ohm | |

Parameters | Values |

14 | |

125 | |

0.8 | |

240 A | |

8.6 m-ohm |

Sea state | 4 | 6 |
---|---|---|

Wave | Regular | Regular |

Wave period | 12 s | 12 s |

Wave height | 2 m | 4 m |

Wave length | 40.29% | 40.29% |

The comparison results of the proposed method with the existing method [

Sea state 4 | |||
---|---|---|---|

BAT |
C3 with IC | C2 | C3 with CC |

RMS error | 0.45 kW | 0.38 kW | 0.20 kW |

Max error | 0.78 kW | 0.76 kW | 0.97 kW |

Loss (%) | 0.42 | 0.3 | 0.9 |

DF |
|||

RMS error | 0.4 kW | 0.31 kW | 0.18 kW |

Max error | 0.78 kW | 0.76 kW | 0.97 kW |

Loss (%) | 0.38 | 0.25 | 0.85 |

Existing |
|||

RMS error | 0.3 kW | 0.24 kW | 0.16 kW |

Max error | 0.78 kW | 0.76 kW | 0.97 kW |

Loss (%) | 0.33 | 0.22 | 0.55 |

Proposed | |||

RMS error | 0.2 kW | 0.12 kW | 0.8 kW |

Max error | 0.78 kW | 0.76 kW | 0.97 kW |

Loss (%) | 0.25 | 0.13 | 0.4 |

Sea state 6 | |||

BAT |
C3 with IC | C2 | C3 with CC |

RMS error | 199 kW | 140 kW | 122 kW |

Max error | 561 kW | 583 kW | 527 kW |

Loss (%) | 5.8 | 0.4 | 3.85 |

DF |
|||

RMS error | 195 kW | 130 kW | 110 kW |

Max error | 561 kW | 583 kW | 527 kW |

Loss (%) | 5 | 0.38 | 3.18 |

Existing |
|||

RMS error | 191 kW | 125 kW | 108 kW |

Max error | 561 kW | 583 kW | 527 kW |

Loss (%) | 4.36 | 0.32 | 3.02 |

Proposed | |||

RMS error | 185 kW | 110 kW | 105 kW |

Max error | 561 kW | 583 kW | 527 kW |

Loss (%) | 4 | 0.12 | 2.1 |

An enhanced solution for power fluctuations has been developed in this work with an optimized energy management strategy utilizing EHO. In general, the mechanical ship can use fuel, and in the case of the electric ship, energy storage systems are introduced to make the way hybrid. The ZEBRA battery and MAXWELL capacitor are provided for energy management purposes. The loss factor has been reduced, and the states of the sea have been found through the EHO algorithm. As a result, the effectiveness of the proposed regime has been greatly enhanced. The results are taken out for showing better performance, in which thrust, power, and torque have been computed. The results obtained by the proposed method have been compared with the existing methods. At stages 6 and 4, the loss factor was calculated by varying the iteration from 0 to 3000. The loss factor was calculated by existing and proposed techniques, the existing techniques like BAT, dragonfly, firefly, ABC, ACO. The ACO and ABC existing techniques got higher losses when compared to the proposed EHO. Finally, the performance of RMS error has been minimized, and the loss has been reduced. In the future, the HESS can be developed with an artificial intelligence-based metaheuristic optimization approach.

No grant has been received from anywhere.