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首页> 外文会议>International Conference on Ecological Vehicles and Renewable Energies >Multi-objective optimization of a long-haul truck hybrid operational strategy and a predictive powertrain control system
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Multi-objective optimization of a long-haul truck hybrid operational strategy and a predictive powertrain control system

机译：远程卡车混合动力操作策略和预测动力总成控制系统的多目标优化

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An optimum operating strategy is mandatory for early amortization of the expensive Hybrid Electric Vehicle (HEV) powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a huge impact on the Total Cost of Ownership (TCO), which is the key entrepreneurial figure in the transportation business. Combined with route information, a Predictive Cruise Control (PPC) System increases the fuel-saving effects. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a Genetic Algorithm (GA). The contradiction between minimizing the fuel consumption and simultaneously maximizing the vehicle speed in order to decrease time-related fixed costs has to be solved. This leads to a Multi-Objective Problem (MOP). The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of recuperating, boosting, shifting the load point (SLP) and electric drive only. The following methodology helps to answer the search for an optimum control parameter setup combining the operational strategy and the PPC System in long-haul operations. This paper describes the model building, simulation and optimization of a rule-based control strategy. The route profile and fuel consumption of an Internal Combustion Engine (ICE) truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving effects of up to 11% with simultaneously increasing the vehicle speed were accomplished.
机译：对于昂贵的混合动力汽车（HEV）动力总成零件的早期摊销，必须采用最佳的操作策略。特别是在长途卡车的操作中，燃料成本对总拥有成本（TCO）产生了巨大影响，而总拥有成本是运输业务中的关键企业家人物。结合路线信息，预测巡航控制（PPC）系统可提高节油效果。在基于MATLAB / Simulink模型的通用方法中，使用遗传算法（GA）优化了操作策略和PPC。必须解决在最小化燃料消耗与同时最大化车速之间以减少与时间有关的固定成本之间的矛盾。这导致了多目标问题（MOP）。针对并行混合拓扑开发了运行策略，该拓扑包括仅恢复，增压，转移负载点（SLP）和电驱动的节油功能。以下方法有助于回答在远程操作中将操作策略和PPC系统结合在一起的最佳控制参数设置的问题。本文介绍了基于规则的控制策略的模型构建，仿真和优化。在实际测试中对内燃机（ICE）卡车的行驶路线和油耗进行了测量。记录的数据用于模型构建和验证仿真工具。通过优化的参数设置，可实现高达11％的节油效果，同时提高了车速。

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