Abstract
Range anxiety is one of the barriers for the customer acceptance of Battery Electric Vehicles (BEVs). To cope with this limitation, this paper presents a Predictive Energy Management System (PEMS) that can reduce total battery energy consumption by using available up-coming route information such as traffic flow, speed limits and road slope. The developed PEMS contains two optimization layers: the first layer generates a speed profile for the upcoming route that minimizes driving energy, while simultaneously controlling the average driving speed; the second layer reduces the energy consumption of the Heating, Ventilation, and Air Conditioning (HVAC) system, while guaranteeing driver thermal comfort. The proposed PEMS results in an algorithm capable of running in real time, due to the use of simplified vehicle and powertrain component models. Simulation results show potential energy savings of 7.1% compared to a baseline strategy, i.e. a non-predictive energy management system.
| Original language | English |
|---|---|
| Pages (from-to) | 14167-14172 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 53 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 Conference number: 21 https://www.ifac2020.org/ |
Bibliographical note
Funding Information:This project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement no. 769935.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement no. 769935.
Keywords
- Dynamic programming
- Electric vehicles
- Energy management systems
- Optimal control
- Speed control
- Supervisory control
- Temperature control
