TOBEV: Thermally Optimised Battery Electric Vehicle

**TOBEV**或**T**密封**O** *优化**B**电池**E**电动**V**车辆是一个项目，带来了一系列的方法在几个层面上解决热系统的优化：*整体车辆热和能源管理使用最优控制理论*子系统模型预测控制*架构成本优化电池电动汽车的热管理系统被认为是显著的能源消费者。例如，在寒冷和炎热的环境条件下，客舱气候舒适度可以将车辆的电动里程减少近三分之一。已经提出了许多减少热系统能耗的技术，例如热泵，辐射加热器和其他局部加热/冷却解决方案，但是他们通常孤立地考虑每种技术，而没有调整车辆系统的其余部分以利用它们。该项目将从单个技术的角度出发，通过使用整车模型和基于最优控制理论的先进优化方法，考虑整体车辆热能优化。这种方法可以自动建立将新组件和热系统架构集成到整体车辆运行中的最佳方式，允许对组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的优化。系统级优化具有显著增加车辆续航里程的潜力。这些结果将与属性优化相结合，以确定优先级并提供最佳的成本/效益架构，同时显著降低总体成本。最后，该项目将在捷豹路虎I-PACE示范车上演示优化后的解决方案，这是一款同类领先的电动汽车。模型预测控制器将集成到车辆中，用于增加电动里程，同时减少开发时间。

**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.