Case Study of Holistic Energy Management Using Genetic Algorithms in a Sliding Window Approach

Abstract

Energy management systems are used to find a compromise between conflicting goals that can be identified for battery electric vehicles. Typically, these are the powertrain efficiency, the comfort of the driver, the driving dynamics, and the component aging. This paper introduces an optimization-based holistic energy management system for a battery electric vehicle. The energy management system can adapt the vehicle velocity and the power used for cabin heating, in order to minimize the overall energy consumption, while keeping the total driving time and the cabin temperature within predefined limits. A genetic algorithm is implemented in this paper. The approach is applied to different driving cycles, which are optimized by dividing them into distinctive time frames. This approach is referred to as the sliding window approach. The optimization is conducted with two separate driving cycles, the New European Driving Cycle (NEDC) and a recorded real-world drive. These are analyzed with regard to the aspects relevant to the energy management system, and the optimization results for the two cycles are compared. The results presented in this paper demonstrate the feasibility of the sliding window approach. Moreover, they reveal the differences in fundamental parameters between the NEDC and the recorded drive and how they affect the optimization results. The optimization leads to an overall reduction in energy consumption of 3 . 37 % for the NEDC and 3 . 27 % for the recorded drive, without extending the travel time.