Location-based energy management optimization for hybrid hydraulic vehicles
Location-based energy management optimization for hybrid hydraulic vehicles
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DOI：
10.1109/acc.2013.6579870
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
F. A. Bender;M. Kaszynski;O. Sawodny
中科院分区：
文献类型：
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作者：
F. A. Bender;M. Kaszynski;O. Sawodny
混合动力液压车辆是一种很有前途的方法，以提高重型车辆，如垃圾车和城市公交车的燃油效率。传统柴油发动机与液压动力系统的组合允许再生制动，从而降低燃料消耗、减少排放和减少制动器磨损。进一步的改进可以通过能量管理策略的数值优化来实现，即在两个推进系统之间分配期望扭矩。然而，对于这样的策略是有效的，短期预测的驾驶配置文件成为必要的，这是简单地假设存在于大多数以前的工作。对于垃圾车和城市公交车的情况，重复行驶路线的假设是有效的。因此，已经开发了迭代学习驾驶简档的系统。所学习的驾驶简档与特定车辆位置相关联。在基于标准参考循环的仿真研究中验证了预测和优化的结果。
Hybrid hydraulic vehicles are a promising approach towards improving the fuel efficiency of heavy vehicles such as garbage trucks and city buses. The combination of a conventional diesel engine with a hydraulic powertrain allows for regenerative braking which results in reduced fuel consumption, reduced emissions and less brake wear. Further improvements can be achieved by numerical optimization of the energy management strategy, i.e. the distribution of the desired torque among the two propulsion systems. However, for such strategies to be efficient, a short-term prediction of the driving profile becomes necessary, which is simply assumed to exist in most previous work. For the case of garbage trucks and city buses, the assumption of repeatedly driven routes is valid. Therefore, a system that iteratively learns driving profiles has been developed. The learned driving profiles are associated with a particular vehicle location. The results of prediction and optimization are validated in a simulation study based on a standard reference cycle.