Model Predictive Control for Energy Management in Electric Hybrid Vehicles

近年来，汽车行业出现了向电动总成的范式转变，这在很大程度上是由使用时污染物排放的大幅减少推动的，这与一个日益环保的社会高度相关。插电式混合动力汽车包括通常使用的电池驱动的电动总成，以及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里程，但在典型使用中，它们可以合理地预期低于标准内燃车的排放量。这些系统提供的技术优势通过绿色补贴等政策被放大到消费者手中，因此很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并因此累积排放的另一个因素是用于管理电动马达和内燃机功率消耗的控制系统。因此，设计控制系统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一问题，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，建议的研究项目是进一步研究使用MPC来管理电动混合动力汽车的功率，更具体地说，完成以下任务-对混合动力汽车的过程进行建模，包括电池状态、发动机功率图和电机损耗，并将函数逼近应用到所需的精度水平，同时允许期望的优化特性，如凸性。一个特别感兴趣的领域将是电池的充电状态，之前假设电池的充电状态在驾驶周期中不断下降。在公司内部，对电池状态、发动机功率和电机功率的限制以及预测的驾驶员需求的不确定性纳入了能源管理策略。这需要使用概率和硬约束进行稳健的优化。使控制器可在通常用于生产车辆的计算硬件上实现，并对功能和精度进行相应的限制。这将需要使用定制的优化方法与避免大量决策变量的分级控制方案相结合。所有上述目标都是完全新颖的，在文献中没有出现，该项目属于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.