Energy Management Strategy Based on Fast Dynamic Programming for Extender Range Electric Logistics Vehicle Hubei Key Laboratory of Power System Design and Test for Ele

Format:

Book

Conference/Event

Author/Creator:

Wei, Changyin, author.

Contributor:

Liu, Dezheng

Wang, Yichen

Wei, Yiyu

Yang, Dinghao

Conference Name:

SAE 2024 Intelligent and Connected Vehicles Symposium (2024-09-22 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

The computational efficiency of dynamic programming (DP) energy management strategies is enhanced through the discretization of state variables in this study. The upper and lower bounds of SOC (State of Charge) and the SOC variation at each moment are calculated using the maximum and minimum power of the range extender, which eliminates invalid state combinations and significantly reduces the size of the feasible state set. To investigate the impact of different sampling intervals on SOC during various phases, intervals at 1s, 2s, 4s, 5s, and 10s are set for both charge retention and consumption phases. It is revealed that in the consumption phase, different sampling intervals minimally affect SOC, with trajectories closely matching. However, in the charge retention phase, the impact of different sampling intervals on SOC is significant, resulting in considerable differences in SOC trajectories. Additionally, in the charging-discharging (CD) phase, fuel consumption significantly varies with sampling intervals, decreasing as the interval increases. In contrast, during the charge storage (CS) phase, minor differences in fuel consumption are observed due to the larger power of the range extender. The DP computation time in the CD phase is substantially less than in the CS phase, primarily because the feasible domain in the CD phase is smaller. As sampling intervals decrease, computation time increases exponentially, characteristic of the DP algorithm. Sampling intervals are recommended to be increased in practical applications to balance computational accuracy and efficiency. This research provides an efficient computational approach for DP energy management strategies and uncovers the impact patterns of sampling intervals on SOC stability and fuel consumption, offering theoretical and practical guidance for the design of energy management strategies

Notes:

Vendor supplied data

Publisher Number:

2024-01-7039

Access Restriction:

Restricted for use by site license