Model Predictive Control for Energy Management in Electric Hybrid Vehicles

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于使用时污染物排放的大幅减少，这与日益注重环保的社会高度相关。插电式混合动力汽车既包括典型使用的电池供电的电力传动系统，也包括在电力完全耗尽时使用的传统内燃机。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比标准内燃机汽车的排放量要少。这些系统提供的技术优势通过诸如绿色补贴等政策被消费者放大，因此可能在未来几年成为汽车市场的关键。影响混合动力汽车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功耗的控制系统。设计一个控制系统来优化机械技术的性能，因此是整个系统的基础。已经使用了几种方法来解决这个问题，其中最优控制公式特别成功。这些公式依次分为三组：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，拟议的研究项目是进一步研究MPC在电动混合动力汽车中管理功率的使用，更具体地说，是为了完成以下任务——对混合动力汽车过程建模，包括电池状态、发动机功率图和电动机损耗，并将函数近似应用于所需的精度水平，同时允许理想的优化特性，如凹凸性。一个特别感兴趣的领域将是电池的充电状态，它之前被认为是在驾驶循环中不断减少的。将电池状态、发动机功率和电动机功率的限制以及预测驾驶员需求的不确定性纳入能源管理策略。这需要具有概率和硬约束的稳健优化。使控制器可在通常用于量产车辆的计算硬件上实现，在功能和精度上有相应的限制。这将使得有必要使用定制的优化方法与避免大量决策变量的分层控制方案相结合。上述所有目标都是完全新颖的，没有出现在文献中，这个项目属于EPSRC能源研究领域。

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.