scholarly journals An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 810
Author(s):  
Yongbing Xiang ◽  
Xiaomin Yang
Keyword(s):  
Ant Colony Optimization ◽  
Fuel Consumption ◽  
Energy Management ◽  
Management Strategy ◽  
Layer Structure ◽  
Ant Colony ◽  
Mode Switching ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Target Value

In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.

Optimal energy management of a fuel cell-battery-supercapacitor-powered hybrid tramway using a multi-objective approach

2019 ◽  
Vol 234 (5) ◽  
pp. 511-523 ◽  
Author(s):  
Han Zhang ◽  
Jibin Yang ◽  
Jiye Zhang ◽  
Pengyun Song ◽  
Ming Li
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Management Strategy ◽  
Operating Cost ◽  
Cell System ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Optimal Energy ◽  
Optimal Energy Management ◽  
Fuel Costs

Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

scholarly journals Adaptive Rule-Based Energy Management Strategy for a Parallel HEV

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4472 ◽  
Author(s):  
Rishikesh Mahesh Bagwe ◽  
Andy Byerly ◽  
Euzeli Cipriano dos Santos ◽  
Ben-Miled
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Duty Cycle ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Detailed Knowledge ◽  
Energy Management Strategy ◽  
Rule Based ◽  
Engine Efficiency ◽  
Efficiency Map

This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium- and heavy-duty vehicles. ARBS can be deployed online to optimize fuel consumption without any detailed knowledge of the engine efficiency map of the vehicle or the entire duty cycle. The proposed strategy improves upon the established Preliminary Rule-Based Strategy (PRBS), which has been adopted in commercial vehicles, by dynamically adjusting the regions of operations of the engine and the motor. It prevents the engine from operating in highly inefficient regions while reducing the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink®, both the proposed ARBS and the established PRBS strategies are compared over an extended duty cycle consisting of both urban and highway segments. The results show that ARBS can achieve high MPGe with different thresholds for the boundary between the motor region and the engine region. In contrast, PRBS can achieve high MPGe only if this boundary is carefully established from the engine efficiency map. This difference between the two strategies makes the ARBS particularly suitable for aftermarket hybridization where full knowledge of the engine efficiency map may not be available.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

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.

Energy Management System Research for Wind-PV-ES Hybrid Microgrid Based on Storage Battery

2013 ◽  
Vol 660 ◽  
pp. 139-145
Author(s):  
Lei Xue ◽  
Li Bin Wang ◽  
Zhi Gang Wang ◽  
Shu Ying Li
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Mode Switching ◽  
Storage Battery ◽  
Energy Management Strategy ◽  
Hybrid Energy ◽  
Hybrid Microgrid ◽  
Flow Charts ◽  
Micro Grid ◽  
Bus Voltage

Energy management strategy is important to keep microgrid stable. In this paper, energy management strategy of Wind-PV-ES hybrid microgrid is proposed. Due to the power output of wind and PV are unknown quantities, the key point of Wind-PV-ES hybrid energy management lies in the energy management of storage battery. The flow charts of energy management strategy are given in detail and Wind-PV-ES microgrid model is built with DigSILENT/PowerFactory. Then the transition state simulation as to the micro-grid mode switching process is carried out. The result shows that the proposed energy management strategy could keep connected bus voltage and micro-grid frequency stable in grid-connected mode, islanding mode and during micro-grid mode switching.

Adaptive Equivalent Factors of Multi-Objective Energy Management for Fuel Cell Hybrid Electric Vehicles

2020 ◽  
Author(s):  
Yan Ma ◽  
Jian Chen ◽  
Junmin Wang
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Cell System ◽  
Fuel Cell System ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Equivalent Factor

Abstract In this paper, a multi-objective energy management strategy with an adaptive equivalent factor is proposed to improve the fuel economy, system durability, and charge-sustenance performance of fuel cell hybrid electric vehicles. Firstly, the total hydrogen consumption and degradation cost of power sources can be calculated by flexible empirical models. Then, the multi-objective optimization problem can be transformed into an objective function, which can be solved by quadratic programming to improve the real-time performance. Furthermore, an adaptive Unscented Kalman filter is designed to estimate the aging state of the fuel cell system. The equivalent factor in the objective function can be adaptively updated by the estimated aging state, which can balance the conflict between the fuel economy and the system durability while keeping the state-of-charge in an ideal range. Finally, simulation results show that when the fuel cell system is obviously damaged during the operation, the proposed energy management strategy still can minimize the total cost and maintain the charge-sustenance performance under different driving cycles compared with other methods.

Adaptive parameter optimal energy management strategy based on multi-objective optimization for range extended electric vehicle

2021 ◽  
pp. 095440702110464
Author(s):  
Hanwu Liu ◽  
Yulong Lei ◽  
Yao Fu ◽  
Xingzhong Li
Keyword(s):  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Loss Rate ◽  
Multi Objective Optimization ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Adaptive Parameter ◽  
Point Control ◽  
Optimal Energy Management

With the aim of economy improvement, emission reduction and prolonging the battery service life, an adaptive parameter optimal energy management strategy is proposed for range extended electric vehicle and a method of multi-objective optimization (MOO) is proposed. Firstly, two strategies based on different threshold parameter types, namely velocity-switch-based multi-operation-point control strategy (MCS v–b) and power-switch-based multi-operation-point control strategy (MCS p–b) are designed. Then, the oil-electric conversion loss rate, comprehensive exhaust emission, and battery capacity loss rate are selected as the optimization objectives. The barebones multi-objective particle swarm optimization is applied in MCS v–b and MCS p–b for solving the MOO problem. The simulation results show a clear conflict that three optimization objectives cannot be optimal under the same solution. And then, the individual with optimal comprehensive objective is taken as the final optimization solution to evaluate the performance of the proposed methodology. As expected, the proposed MCS p–b has a positive effect on prolonging the battery service life while ensuring high fuel economy and low emission. Experimental test results thoroughly validate the proposed approach and this result can be used to improve comprehensive performance levels.

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