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

TL;DR

This paper evaluates rule-based energy management strategies for hybrid vehicles using real-world data and dynamic programming, highlighting discrepancies between model-based and actual vehicle controller feedback.

Contribution

It introduces an optimization approach based on vehicle measurement data and compares it with rule-based strategies to identify improvement areas.

Findings

Rule-based strategies are less efficient than optimized ones.

OBD usage can increase energy consumption.

Real-world data improves energy management optimization.

Abstract

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.