JOURNAL ARTICLE

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

Abstract

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.

Keywords:
Model predictive control Energy management Automotive engineering Computer science Efficient energy use Electric vehicle Battery (electricity) Energy management system Energy consumption Schedule Control (management) Energy (signal processing) Power (physics) Engineering Electrical engineering

Metrics

5
Cited By
0.32
FWCI (Field Weighted Citation Impact)
24
Refs
0.60
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Battery Technologies Research
Physical Sciences →  Engineering →  Automotive Engineering
Electric Vehicles and Infrastructure
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Electric and Hybrid Vehicle Technologies
Physical Sciences →  Engineering →  Automotive Engineering
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