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

数据驱动的电动/混合动力汽车热管理以实现最佳能源消耗

• 批准号: 2683123
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2683123
• 关键词: Data; driven; Thermal; Management; Electric

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.

数据驱动的建模和分析已成为一种革命性的概念和现代科学和工程工具，用于对典型的非线性、动态、多尺度和高维的复杂系统进行建模、预测和控制。随着汽车行业电气化程度的不断提高和严格的立法要求，能量流的管理在电动/混合动力汽车（EV/HV）的开发过程中提出了重大挑战。热分析和管理是现代 EV/HV 设计和交付的一个组成部分。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，通过优化发动机、变速箱、电池和电机温度的热平衡来实现更高的性能，同时保持对驾驶室的快速全气候控制，以提供驾驶员舒适度。该博士课程将重点关注数据驱动建模以及使用数据分析将热数据转换为预测控制算法。这对于获得未来电动/混合动力汽车的最佳能源消耗解决方案至关重要。该博士将与捷豹路虎的团队密切合作，参与针对工业解决方案优化的降阶 EV/HV 热能和控制模型的开发。