Next Article in Journal
Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode
Next Article in Special Issue
Development of Technical Regulations for Fuel Cell Motorcycles in Japan—Hydrogen Safety
Previous Article in Journal / Special Issue
Electric Vehicle Fast Charging Needs in Cities and along Corridors
Open AccessArticle

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

Institute of Automotive Technology, Technical University Munich, 85748 Garching, Germany
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2019, 10(2), 46; https://doi.org/10.3390/wevj10020046
Received: 18 April 2019 / Revised: 25 May 2019 / Accepted: 4 June 2019 / Published: 18 June 2019
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. View Full-Text
Keywords: energy management system; genetic algorithm; battery electric vehicle; New European Driving Cycle (NEDC); multi-objective optimization energy management system; genetic algorithm; battery electric vehicle; New European Driving Cycle (NEDC); multi-objective optimization
Show Figures

Figure 1

MDPI and ACS Style

Minnerup, K.; Herrmann, T.; Steinstraeter, M.; Lienkamp, M. Case Study of Holistic Energy Management Using Genetic Algorithms in a Sliding Window Approach. World Electr. Veh. J. 2019, 10, 46.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop