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Model Predictive Control for Energy Management in Electric Hybrid Vehicles

电动混合动力汽车能量管理的模型预测控制

基本信息

批准号：
1793329
负责人：
金额：
--
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1793329
关键词：
Model Predictive Control Energy Management

项目摘要

In recent years, the automotive industry has seen a paradigm shift towards electric powertrains, largely driven by the considerable reduction of pollutant emissions at the point of use, something highly relevant to an increasingly environmentally conscious society. Plug in hybrid electric vehicles incorporate both a battery powered electric powertrain for typical use, and a conventional internal combustion engine for use if the electric power is completely depleted. Therefore, whilst the overall emissions of the car depend on several factors, including the source of the electric power and the cumulative distance driven, in typical use they can reasonably be expected to be less than that of a standard internal combustion car. The technological advantages these systems provide are amplified to the consumer by policies such as green subsidies, and therefore are likely to be key in the automotive market for years to come.A further factor that influences the efficiency, and therefore cumulative emissions of a hybrid vehicle is the control system used to manage the power consumption of both the electric motor and internal combustion engine. Design of a control system to optimise the performance of the mechanical technologies is therefore a fundamental to the overall system. Several approaches have been used to tackle this issue, of which optimal control formulations have been particularly successful. These formulations in turn fall into three groups: Dynamic Programming DP, Pontryagin Minimum Principle PMP and Model Predictive Control MPC.The proposed research project is therefore to further investigate the use of MPC to manage power in an electric hybrid vehicle, and more specifically to complete the following tasks - Model the hybrid vehicle processes including battery state, engine power maps and electric motor losses, and apply function approximation to the required level of accuracy whilst allowing desirable optimisation properties such as convexity. A particular area of interest will be the charge state of the battery, which has previously assumed to constantly decrease through the driving cycle.Incorporate constraints on the battery state, engine power and electric motor power as well as uncertainty in the predicted driver demand into the energy management strategy. This calls for a robust optimisation with probabilistic and hard constraints.Make the controller implementable on computing hardware typically available in production vehicles, with corresponding limits on functionality and precision. This will make it necessary to use a bespoke optimisation method in conjunction with a hierarchical control scheme that avoids large numbers of decision variables.All of the above aims are completely novel and do not appear in the literature, and this project falls within the EPSRC energy research area.

近年来，汽车行业已经看到了向电动动力系统的范式转变，这在很大程度上是由使用时污染物排放的大幅减少所推动的，这与日益环保的社会高度相关。插电式混合动力电动车辆结合了用于典型用途的电池供电的电动动力系和用于在电力完全耗尽时使用的常规内燃机。因此，虽然汽车的总排放量取决于几个因素，包括电力来源和累计行驶距离，但在典型使用中，它们可以合理地预期低于标准内燃机汽车的排放量。这些系统提供的技术优势通过诸如绿色补贴等政策被放大到消费者，因此可能在未来几年成为汽车市场的关键。影响混合动力车辆的效率和累积排放的另一个因素是用于管理电动机和内燃机两者的功率消耗的控制系统。因此，控制系统的设计以优化机械技术的性能是整个系统的基础。已经使用了几种方法来解决这个问题，其中最佳控制配方特别成功。这些制剂又分为三类：动态规划DP，庞特里亚金最小原理PMP和模型预测控制MPC。因此，所提出的研究项目是进一步研究使用MPC来管理电动混合动力车辆中的功率，并且更具体地说，完成以下任务-对混合动力车辆过程建模，包括电池状态，发动机功率图和电动机损耗，并将函数近似应用到所需的精度水平，同时允许期望的优化特性，例如凸性。一个特别的关注领域是电池的充电状态，之前假设电池的充电状态在驾驶循环中不断下降。将电池状态、发动机功率和电动机功率的约束以及预测驾驶员需求的不确定性纳入能源管理策略。这就需要在概率和硬约束条件下进行鲁棒优化。使控制器可在生产车辆中通常可用的计算硬件上实现，并对功能和精度进行相应的限制。这将使得有必要使用一个定制的优化方法结合一个分层控制方案，避免了大量的决策变量。所有上述目标是完全新颖的，并没有出现在文献中，这个项目属于EPSRC能源研究领域的福尔斯。