Energy Management Strategy Based on Fast Dynamic Programming for Extender Range Electric Logistics Vehicle

<div class="section abstract"><div class="htmlview paragraph">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.</div></div>

Authors

Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2024-12-12
DOI
https://doi.org/10.4271/2024-01-7039
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Energy Management Strategy Based on Fast Dynamic Programming for Extender Range Electric Logistics Vehicle

Dezheng Liu, Yanji Wei, Changyin Wei, Yichen Wang et al.
SAE technical papers on CD-ROM/SAE technical paper series
Electric and Hybrid Vehicle Technologies
article

Energy Management Strategy Based on Fast Dynamic Programming for Extender Range Electric Logistics Vehicle

Dezheng Liu, Yanji Wei, Changyin Wei, Yichen Wang, Dixiong Yang
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">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.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Affordable and clean energy
Openalex Percentile: Top 76%
Electric and Hybrid Vehicle Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.