Optimal energy use in mobile applications with storage

Energy Management;Model Predictive Control;Optimal Control;Hydraulic Hybrid;Thermal Hybrid

Deppen, Tim O.

关键词: Energy Management;    Model Predictive Control;    Optimal Control;    Hydraulic Hybrid;    Thermal Hybrid;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/44290/Tim_Deppen.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

With growing demands and dwindling resources, the need for energy efficiency is being felt in all sectors.The transportation sector is one of the largest consumers of energy and to reduce fuel consumption and greenhouse gas emissions, hybrid mobile power systems are seeing increased use.A hybrid mobile power system is any vehicle that includes a power source and a means of storing that power.These vehicles offer an opportunity for improved efficiency by partially decoupling power generation from demand, enabling more efficient operation.This decoupling is achieved via energy storage which offers new opportunities for how energy is utilized.To realize the potential of hybrid architectures, an energy management strategy (EMS) is needed to regulate the generation, distribution, and storage of energy.Hybrid vehicles span wide power and weight scales from small passenger vehicles to large delivery trucks and the energy storage mechanisms come in many domains including mechanical, thermal, and electrical.Therefore, if one is to enable effective wide spread use of hybrid vehicles, a method for designing EMS’s which is effective across applications and energy domains is needed.In this work a procedure for design of EMS’s is given, which is intended to be generalizable to the entire class of hybrid mobile power systems.This procedure begins by decomposing the vehicle operation into modes characterized by which power sources are needed.Then convex quadratic objective functions are designed for each mode which attempt to maximize operational efficiency while meeting a performance goal.Finally, a supervisory logic is used to regulate switching between modes.The model predictive control (MPC) framework is used to setup the optimization problem within each mode as a receding horizon optimal controller which can be implemented in real-time.The proposed method facilitates online implementation because it constrains the optimization problem to be convex and quadratic.Furthermore, MPC allows flexibility in how much knowledge one assumes about the future, enabling this approach to be applied equally well to highly uncertain applications, like passenger vehicles, and well known systems, like city busses.The generalizability of the proposed method is tested through application in two different hybrid vehicles; a series hydraulic hybrid vehicle (SHHV) and a refrigerated delivery truck with thermal storage.The SHHV is a passenger vehicle which uses a hydrostatic transmission with a high pressure gas charged accumulator for energy storage.The goal of this system is to meet the driver’s speed demand while maximizing operational efficiency.This case study includes experimental validation of the EMS performance using a hardware-in-the-loop system.The refrigerated delivery truck uses a vapor compression cycle system that has been augmented with thermal storage to maintain a desired box temperature while maximizing operational efficiency.These case studies employ different architectures, different energy domains, and different degrees of knowledge of the system and duty cycle.However, the proposed EMS design method is able to yield energy savings for both.