A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control

2022 ◽  
pp. 147509022110689
Author(s):  
Yupeng Yuan ◽  
Mingshuang Chen ◽  
Jixiang Wang ◽  
Wanneng Yu ◽  
Boyang Shen
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategy ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Threshold Control ◽  
Rule Based ◽  
Dynamic Programing ◽  
Simulation Results

The energy-saving characteristics of diesel-electric series hybrid ships largely depend on their energy management strategy. In this paper, a strategy that combines dynamic programing and model predictive control (DP-MPC) is proposed to solve the energy management problems of diesel-electric hybrid ships. The DP-MPC strategy has considered some typical working conditions of a ship, and the corresponding influence of white noise disturbance on the control strategy was studied. The simulation results show that the DP-MPC strategy has an excellent anti-interference capability. The control performance of the DP-MPC strategy is then further analyzed and compared with the rule-based logic threshold control strategy. The simulation results show that the proposed DP-MPC strategy can save 2.5% of the fuel consumption and has a better anti-interference capability than the rule-based control strategy.

A Model Predictive Control-Based Energy Management Strategy Considering Electric Vehicle Battery Thermal and Cabin Climate Control

2020 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Critical Role ◽  
Peak Load ◽  
Climate Control ◽  
Energy Management Strategy ◽  
Battery Thermal Management System

Abstract The energy management strategy plays a critical role in scheduling the operations and enhancing the overall efficiency for electric vehicles. This paper proposes an effective model predictive control-based (MPC) energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system for electric vehicles (EVs). We aim to improve the overall energy efficiency, while retaining soft constraints from both BTMS and AC systems. It is implemented by optimizing the operation and discharging schedule to avoid peak load and by directly utilizing the regenerative power instead of recharging. Compared to the systematic performance without any control coordination between BTMS and AC, results reveal that there are a 4.3% reduction for the recharging energy, and a 6.5% improvement for the overall energy consumption that gained from the MPC-based energy management strategy. Overall the MPC-based energy management is a promising solution to enhance the efficiency for electric vehicles.

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