TOBEV: Thermally Optimised Battery Electric Vehicle

**TOBEV**或**T**完全优化**B**电池**E**电气**V**Ehicle是一个项目，它将一系列方法结合在一起，在几个层面上解决热系统的优化：*使用最优控制理论的整体车辆热和能量管理*子系统模型预测控制*体系结构成本优化电池电动汽车中的热管理系统被认为是重要的能源消耗。例如，众所周知，在寒冷和炎热的环境条件下，座舱气候舒适性会使车辆的电动里程减少近三分之一。已经提出了许多降低热系统能耗的技术，如热泵、辐射加热器和其他局部供暖/制冷解决方案，但他们往往孤立地考虑每种技术，而不是调整车辆系统的其余部分来利用它们。本项目将不再考虑单独的技术，而是通过使用整车模型和基于最优控制理论的高级优化方法来考虑整体车辆热能优化。这种方法可以自动建立将新部件和热系统架构集成到整体车辆运行中的最佳方式，允许对部件选择进行可靠的比较--因为它们在目标车辆中得到了充分优化。系统级优化有可能显著增加车辆续航里程。这些结果将与属性优化相结合，以确定优先级并提供最佳成本/效益架构，同时显著降低总体成本。最后，该项目将在一款车型领先的电动汽车捷豹路虎i-Pace汽车中演示优化解决方案。模型预测控制器将被集成到车辆中，用于增加电动续航里程，同时还减少开发时间。

**TOBEV** or **T**hermally **O**ptimised **B**attery **E**lectric **V**ehicle is a project that brings a range of methodologies together to address the optimisation of the thermal system at several levels:* Overall vehicle thermal and energy management using Optimal Control Theory* Subsystem model predictive controls* Architecture cost optimisationThe thermal management systems in battery electric vehicles are known to be significant energy consumers. For example, the cabin climate comfort is known to reduce the electric range of a vehicle by almost 1/3rd in cold and hot ambient conditions.There have been many technologies proposed for reducing the thermal system energy consumption such as heat pumps, radiative heaters and other localised heating/cooling solutions however they often consider each technology in isolation without adapting the rest of the vehicle systems to take advantage of them.This project will stand back from the individual technologies and consider the holistic vehicle thermal energy optimisation, by using models of the full vehicle combined with advanced optimisation approaches based on Optimal Control theory. This approach can automatically establish the best way of integrating new components and thermal system architectures into the overall vehicle operation, allowing robust comparisons of component choices -- since they are fully optimised in the target vehicle. The system level optimisation has the potential to increase the vehicle range significantly.These results will be combined with an attribute optimisation to prioritise and deliver the best cost/benefit architectures, with significant reduction in overall cost.Finally, the project will demonstrate the optimised solution in a demonstrator JLR I-PACE vehicle, a class leading electric vehicle. A model predictive controller will be integrated into the vehicle and used to deliver the increase in electric range whilst also reducing development times.