传统、混合和插电式电动车的车辆动力总成连接路线优化
Vehicle Powertrain Connected Route Optimization for Conventional, Hybrid and Plug-in Electric Vehicles
AI总结：
本文提出了一种基于车辆动力总成信息的路线优化策略，考虑车辆类型、电池状态和交通条件对行程成本的影响，通过优化得到更低成本的路径。
摘要：
大多数导航系统利用卫星数据提供最短距离、最短时间或高速公路优先路径。然而，当为先进车辆制定路线决策时，影响成本的因素还包括车辆动力总成类型、电池电量状态（SOC）以及交通条件下组件效率的变化，这些因素传统路线系统未考虑。路线决策中距离与交通之间的权衡对行程成本的影响可能因车辆技术类型和组件动态而变化。因此，最低成本路径可能不同于最短距离或最短时间路径。本文提出了一种新的路线策略，利用上述信息使决策过程产生最低成本路径。我们整合车辆动力总成动态到路线优化中，并将其称为车辆动力总成连接路线优化（VPCRO）。我们发现，当使用VPCRO代替最短距离策略时，所有类型的车辆动力总成的最优路径可能会显著变化。当车辆类型为传统车辆（CV）和电动车辆（EV）时，分别有约81%和58%的行程被不同的最优路径取代。改变的路线在平均上减少了15%至最大60%的旅行成本，对于EVs平均减少了6%至最大30%。此外，观察到当初始电池SOC从90%降至60%和40%时，插电式混合电动车的最优路径有3%和10%的不同。论文表明，利用车辆动力总成的感官信息进行路线优化对最小化旅行成本起着重要作用。
英文摘要：
Most navigation systems use data from satellites to provide drivers with the shortest-distance, shortest-time or highway-preferred paths. However, when the routing decisions are made for advanced vehicles, there are other factors affecting cost, such as vehicle powertrain type, battery state of charge (SOC) and the change of component efficiencies under traffic conditions, which are not considered by traditional routing systems. The impact of the trade-off between distance and traffic on the cost of the trip might change with the type of vehicle technology and component dynamics. As a result, the least-cost paths might be different from the shortest-distance or shortest-time paths. In this work, a novel routing strategy has been proposed where the decision-making process benefits from the aforementioned information to result in a least-cost path for drivers. We integrate vehicle powertrain dynamics into route optimization and call this strategy as Vehicle Powertrain Connected Route Optimization (VPCRO). We find that the optimal paths might change significantly for all types of vehicle powertrains when VPCRO is used instead of shortest-distance strategy. About 81% and 58% of trips were replaced by different optimal paths with VPCRO when the vehicle type was Conventional Vehicle (CV) and Electrified Vehicle (EV), respectively. Changed routes had reduced travel costs on an average of 15% up to a maximum of 60% for CVs and on an average of 6% up to a maximum of 30% for EVs. Moreover, it was observed that 3% and 10% of trips had different optimal paths for a plug-in hybrid electric vehicle, when initial battery SOC changed from 90% to 60% and 40%, respectively. Paper shows that using sensory information from vehicle powertrain for route optimization plays an important role to minimize travel costs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
