强并联电气化动力总成的综合能源足迹基准测试
Comprehensive Energy Footprint Benchmarking of Strong Parallel Electrified Powertrain
AI总结：
本研究针对强并联电气化商用车，采用PS3框架与帕累托最优分析开展能源管理优化，实现燃油消耗降低7%、NOx减排29%，为动力总成能效提升提供参考。
摘要：
本工作针对一款强并联电气化商用车开展了综合能源管理与深度能源足迹分析。我们采用PS3框架、经真实工况验证的动力总成系统模型以及帕累托最优分析，以优化燃油消耗与有害污染物排放。该方法针对一款并联混合动力 pickup 取送货车，对13个状态量与4个控制杆进行动态优化，涉及多个子系统间的复杂相互作用。这些子系统呈现不同时间尺度的热、电、机械动力学特性，包含运动学与组合约束、整数及实值变量、插值查询表与数据图谱。通过精细优化燃油与NOx排放得到帕累托最优解，以明晰电气化动力总成的能源足迹。结果展示了动力总成子系统间丰富的分析维度与复杂相互作用，发现相较于粗粒度建模的动力总成系统方案，其燃油消耗改善7%，污染物NOx减排29%。
英文摘要：
This work presents comprehensive energy management and in-depth energy footprint analysis of an electrified strong parallel commercial vehicle. We use the PS3 framework, validated real-world powertrain system models, and Pareto-optimal analysis to optimize fuel consumption and harmful pollutant emissions. The approach involves dynamic optimization of 13 states and 4 control levers with complex interactions between multiple subsystems for a parallel hybrid electric pick-up and delivery truck. These subsystems exhibit thermal, electrical, and mechanical dynamics at different time scales, and contain kinematic and combinatorial constraints, integer- and real-valued variables, interpolated look-up tables, and data maps. A Pareto-optimal solution is found by carefully optimizing fuel and NOx emissions to understand the energy footprint of the electrified powertrain. The presented results exhibit rich analysis and complex interactions among the powertrain subsystems to unearth a 7% improvement in its fuel consumption and 29% pollutant NOx reduction when compared to solution from a coarsely modeled powertrain system.
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