Energy consumption

Quantum mechanics

Algorithm

Vehicle dynamics

Engineering

Dynamic programming

Biology

Rule-based system

Statistics

Energy management system

Control engineering

Computer security

Energy (signal processing)

Driving cycle

Power (physics)

Computer science

Electric vehicle

Controller (irrigation)

Energy management

Physics

Mathematics

Hybrid vehicle

Agronomy

Automotive engineering

Artificial intelligence

System dynamics

Electrical engineering

Key (lock)

Silniki Spalinowe/Combustion Engines

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming