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

Yuan Liu, Jie Zhang
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引用次数: 1

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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基于模型预测控制的电动汽车电池热和座舱气候控制能量管理策略
能源管理策略对电动汽车的运行调度和整体效率的提高起着至关重要的作用。本文提出了一种有效的基于模型预测控制(MPC)的能量管理策略,以同时控制电动汽车电池热管理系统(BTMS)和座舱空调(AC)系统。我们的目标是提高整体能源效率,同时保留BTMS和AC系统的软约束。通过优化运行和放电计划以避免峰值负荷,直接利用再生电力而不是再充电来实现。结果表明,与没有任何BTMS和AC控制协调的系统性能相比,基于mpc的能源管理策略使充电能量减少了4.3%,总能耗提高了6.5%。总的来说,基于mpc的能源管理是一种很有前途的提高电动汽车效率的解决方案。
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