Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach
Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach
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自动驾驶电动公交车预测自适应巡航控制的多目标权衡优化：网络物理能源系统方法
DOI：
10.1016/j.apenergy.2021.117385
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发表时间：
2021-10
期刊：
影响因子：
11.2
通讯作者：
Jia Chunchun
中科院分区：
文献类型：
--
作者：
Shi Man;He Hongwen;Li Jianwei;Han Mo;Jia Chunchun
近年来，信息物理系统（CPS）作为下一代工业应用的前沿技术，正在迅速发展并激发了许多应用领域的灵感。自动驾驶电动巴士（AEB）将车辆动力学中的通信、感知和控制集成在一起，是一种典型的 CPS。然而，车辆信息物理系统中却忽略了能量管理。因此，采用了一种新颖的信息物理能源系统（CPES）方法，该方法将信息系统与物理系统进行深度集成和交互，用于巡航控制中的能量管理。在新的CPES框架下，优化了不同驾驶环境下的能耗和电池容量衰减。仿真结果表明，权衡优化控制算法可以通过优化电机运行模式来保持电池健康，同时对能耗略有惩罚。系统总体成本效益分析表明，与仅优化功耗的策略相比，该方法的电池使用寿命提高了约41.59%，即使稍微牺牲功耗，整车经济性也提高了约10.08%。此外，与仅优化能耗的情况相比，等效行驶距离显着延长达70.87%。此外，采用CPES框架的AEB不仅使主车与前车保持在安全距离内，还优化了运动规划。结果验证了基于CPES的优化框架的可行性和有效性，并展示了权衡优化能源管理策略的优势。
Recently, Cyber-Physical System (CPS) has served as a cutting-edge technology for next-generation industrial applications, and is developing rapidly and inspires many application domains. The autonomous electric bus (AEB) that integrates the communication, perception, and control within vehicle dynamics is a typical CPS. However, the energy management is ignored in the vehicle cyber-physical system. Thus, a novelty cyber-physical-energy system (CPES) approach with deep integration and interaction of the cyber system with physical system for the energy management used cruising control is imposed. Under the new CPES framework, the energy consumption and battery capacity degradation are optimized in different driving environment. Simulation results show that the tradeoff optimization control algorithm can keep battery health by optimizing motor operating mode with a slightly penalty on the energy consumption. The total system cost effective analysis shows that the battery service lifetime is improved by about 41.59% with the proposed method, and even with the slightly sacrifice of power consumption, the whole vehicle economy is improved by about 10.08%, compared with the strategy optimizing the power consumption only. Additionally, the equivalent driving distance is significantly extended up to 70.87% when compared to the case that only energy consumption is optimized. Besides, the AEB with CPES framework not only keeps the host vehicle within the safe distance with the preceding vehicle, but optimizes the motion planning as well. The results validate the feasibility and effectiveness of the CPES-based optimization framework, and demonstrate the advantages of the tradeoff optimization energy management strategy.
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影响因子：
7
作者：
L. Tang;G. Rizzoni;S. Onori
通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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