Fuzzy logic-model predictive control energy management strategy for a dual-mode locomotive

2022 ◽  
Vol 253 ◽  
pp. 115111
Author(s):  
Rusber Rodriguez ◽  
João Pedro F. Trovão ◽  
Javier Solano
Keyword(s):  
Fuzzy Logic ◽  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Management Strategy ◽  
Logic Model ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Fuzzy Logic Model

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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