Inverse optimal control of electric vehicle cabin heating loop

Abstract

The range of electric vehicles (EVs) is worsened in extreme conditions, due to excessive energy consumption by the thermal management. Thus, more efficient thermal management is needed. Among different loops that provide thermal conditioning for different components, cabin heating loop is chosen as scope of this study. In this study, it is aimed to reduce power consumption of actuators while maintaining the comfort. Model-based control strategies are one of the options to improve thermal management which rely on a mathematical model. Among different approaches for thermal modelling, gray-box modeling technique is selected and equations are obtained. To perform parameter estimation at different temperatures, tests are conducted at -20, -5, and 10°C on TOFAŞ's prototype electric vehicle. Then, genetic algorithm is used to estimate parameters. Then, the model is transformed into input-affine form. After obtaining the model, Inverse Optimal Control was applied. As a consequence, an appropriate P matrix needed to be sought. The Big Bang-Big Crunch optimization algorithm was used for the search. Optimization algorithm was run for one ambient temperature and a P matrix was obtained which provided inverse optimal control for all ambient temperatures. The contribution of the work can be emphasized as: i. Inverse Optimal Control is applied to thermal management of EVs for the first time ii. a novel model in input-affine form for cabin heating loop of a prototype vehicle is obtained. Although this work presents a particular case for thermal management of EVs, the method can also be applied to other loops.