A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm
A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm
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电动汽车混合储能系统能量管理策略的数学表征以及基于遗传算法的实时优化
DOI：
10.1016/j.apenergy.2017.02.022
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发表时间：
2017-04
期刊：
影响因子：
11.2
通讯作者：
M. Wieczorek;M. Lewandowski
中科院分区：
文献类型：
--
作者：
M. Wieczorek;M. Lewandowski
本文提出了一种简单且易于优化的电动汽车混合储能系统（HESS）能源管理策略（EMS）的数学表示。 HESS 中每个设备的功率作为负载功率的连续函数（称为γ）提供。基于所提出方法的两种策略，一种结合了γ函数（GBS）的固定系数，另一种采用向后时间窗口通过遗传算法（GAS）实时优化的系数，经过测试并与基于规则的策略（RBS）和电池存储系统进行了比较。这些计算是针对配备 LiFePO4 电池超级电容器 HESS 的电动汽车进行的。分析的参数包括：电池的能耗、RMS 和最大电流速率，以及采用 HESS 的电动汽车和电池供电电动汽车的循环成本。该分析是根据驱动循环速度和电池模块的内阻进行的。所得结果表明，与电池供电的EV相比，GBS和GAS能够在NEDC中将RMS电流率降低40%，并且最大电流率不超过标称值。 GAS 旨在最大限度地减少能源消耗。它在低速循环中获得最佳结果。
This paper proposes a simple and easily optimizable mathematical representation of an energy management strategy (EMS) for the hybrid energy storage system (HESS) in EV. The power of each device in the HESS is provided as a continuous function of load power calledγ. Two strategies based on the proposed method, one incorporating fixed coefficients of theγfunction (GBS) and one with coefficients optimized by a genetic algorithm (GAS) in real-time using a backward time window, are tested and compared to the rule-based strategy (RBS) and battery storage system. The calculations are made for an electric car with a LiFePO4battery-supercapacitor HESS. The analyzed parameters are: energy consumption, RMS and maximum current rates of the battery, and the cycle cost of an EV with HESS and a battery-powered EV. The analysis is made in dependence on drive cycle speed and an internal resistance of the battery module. The obtained results show that the GBS and the GAS are able to reduce the RMS current rate by 40% in the NEDC in comparison to battery-powered EV, as well as that maximum current rates do not exceed nominal values. The GAS aims at the minimization of energy consumption. It obtains best results in low speed cycles.