scholarly journals A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1380 ◽  
Author(s):  
Rui Yang ◽  
Yupeng Yuan ◽  
Rushun Ying ◽  
Boyang Shen ◽  
Teng Long
Keyword(s):  
Solar Energy ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Management Strategy ◽  
Energy System ◽  
Generation System ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New Energy

Due to the pressures caused by the energy crisis, environmental pollution, and international regulations, the largest ship-producing nations are exploring renewable resources, such as wind power, solar energy, and fuel cells to save energy and develop more environmentally-friendly ships. Solar energy has recently attracted a great deal of attention from both academics and practitioners; furthermore, the optimization of energy management has become a research topic of great interest. This paper takes a solar-diesel hybrid ship with 5000 car spaces as its research object. Then, following testing on this ship, experimental data were obtained, a multi-objective optimization model related to the ship’s fuel economy and diesel generator’s efficiency was established, and a partial swarm optimization algorithm was used to solve a multi-objective problem. The results show that the optimized energy management strategy for a hybrid energy system should be tested under different electrical loads. Moreover, the hybrid system’s economy should be taken into account when the ship’s power load is high, and the output power from the new energy generation system should be increased as much as possible. Finally, the diesel generators’ efficiency should be taken into consideration when the ship’s electrical load is low, and the injection power of the new energy system should be reduced appropriately.

scholarly journals Multi-Objective Energy Management Strategy Based on PSO Optimization for Power-Split Hybrid Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2438
Author(s):  
Aimin Du ◽  
Yaoyi Chen ◽  
Dongxu Zhang ◽  
Yeyang Han
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Management Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Test Cycle ◽  
Energy Management Strategy ◽  
Swarm Optimization ◽  
Power Split

The hybrid electric vehicle is equipped with an internal combustion engine and motor as the driving source, which can solve the problems of short driving range and slow charging of the electric vehicle. Making an effective energy management control strategy can reasonably distribute the output power of the engine and motor, improve engine efficiency, and reduce battery damage. To reduce vehicle energy consumption and excessive battery discharge at the same time, a multi-objective energy management strategy based on a particle swarm optimization algorithm is proposed. First, a simulation platform was built based on a compound power-split vehicle model. Then, the ECMS (Equivalent Consumption Minimization Strategy) was used to realize the real-time control of the model, and the penalty function was added to modify the objective function based on the current SOC (State of Charge) to maintain the SOC balance. Finally, the key parameters of ECMS were optimized by using a particle swarm optimization algorithm, and the effectiveness of the control strategy was verified under the WLTC (Worldwide Light-Duty Test Cycle) and the NEDC (New European Driving Cycle). The results show that under the WLTC test cycle, the overall fuel consumption of the whole vehicle was 6.88 L/100 km, which was 7.7% lower than that before optimization; under the NEDC test cycle, the fuel consumption of the whole vehicle was 5.88 L/100 km, which was 9.8% lower than that before optimization.

scholarly journals Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers

2021 ◽  
Vol 12 (4) ◽  
pp. 175
Author(s):  
Ying Huang ◽  
Fachao Jiang ◽  
Haiming Xie
Keyword(s):  
Machine Learning ◽  
Optimal Control ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Management Strategy ◽  
General Purpose ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
New Energy

The new energy of concrete truck mixers is of great significance to achieve energy conservation and emission reduction. Unlike general-purpose vehicles, in addition to driving conditions, upper-mixing system conditions, operation scenarios, and variable loads are the key factors to be considered during the new energy of concrete truck mixers. This study focuses on the machine-learning-based approximate optimal energy management design for a concrete truck mixer equipped with a novel extended-range powertrain from two aspects: trip information and energy management strategy. Firstly, an optimal control database is constructed, which benefits from a global optimization algorithm with dimension reduction for the constrained time-varying two-point boundary value problems with two control variables, and the driving data analysis through machine learning and data-driven methods. Then, different machine-learning-based driving condition identifiers are constructed and compared. Finally, a vehicle mass and power demand of an upper-part system based novel neural network energy management strategy is designed based on a constructed optimal control database. Simulation results show that the intelligent optimization algorithm based on the ML of trip information and energy management is an appropriate way to solve the online energy management problem of the concrete truck mixer equipped with the proposed novel powertrain.

Sign in / Sign up

Export Citation Format

Share Document