Data-driven Thermal Management of Electric/Hybrid Vehicles for Optimum Energy Consumption

数据驱动的建模和分析已经成为一个革命性的概念和现代科学和工程工具，用于建模，预测和控制复杂系统，这些系统通常是非线性，动态，多尺度和高维的。随着汽车工业电气化程度的提高和严格的立法要求，在电动/混合动力汽车（EV/HV）的发展过程中，能量流的管理提出了一个重大挑战。热分析和管理是现代EV/HV设计和交付不可或缺的一部分。借助现代数学方法和人工智能方法，数据驱动的智能热管理和优化使研究人员和工程师能够为电动/混合动力汽车提供控制策略，以实现发动机、变速器、电池和电机温度的优化热平衡，同时保持快速的全气候控制的车厢，以提供驾驶员的舒适性。这个博士课程将侧重于数据-使用数据分析驱动建模并将热数据转换为预测控制算法。这对于未来电动/混合动力汽车获得最佳能耗解决方案至关重要。博士将与捷豹路虎的团队密切合作，参与开发针对工业解决方案优化的降阶EV/HV热能和控制模型。

Data-driven modelling and analysis has become a revolutionary concept and modern scientific and engineering tool to model, predict and control complex systems, which are typically nonlinear, dynamic, multi-scale, and high-dimensional. With the increasing degree of electrification in automotive industry and stringent legislative requirements, the management of energy flow poses a significant challenge during the development of electric/hybrid vehicles (EV/HV). Thermal analysis and management is an integral part of modern EV/HV design and delivery. With modern mathematical methods and artificial intelligence approaches, data-driven intelligent thermal management and optimization allow researchers and engineers to provide control strategies for electric/hybrid vehicles to achieve improved performance due to optimized heat balance of the engine, transmission, battery and motor temperatures, while maintaining fast full-climate control of the cabin to deliver driver comfort.This PhD programme will focus on data-driven modelling and conversion of thermal data into predictive control algorithm using data analytics. This is critical to obtain optimum energy consumptionsolutions for future electric/hybrid vehicles. The PhD will work closely with groups in JLR, taking part in the development of reduced-order EV/HV thermal energy and control models optimised for industrial solutions.