Research on Braking Force Distribution Strategy for Race Cars Based on PID Algorithm

This study proposes a dynamic braking force distribution strategy based on a PID algorithm for Formula Student electric racing cars, addressing the limitations of fixed-ratio distribution methods in adapting to dynamic braking conditions. The strategy utilizes a PID controller targeting the desired slip ratio to dynamically adjust the braking force distribution coefficient (β) between the front and rear axles. The proposed method was validated through co-simulation using CarSim and Simulink, as well as real vehicle testing. Simulation results show a 7.7% reduction in braking distance under emergency braking at 100 km/h with the PID control strategy, while real vehicle testing confirmed a braking distance of 30 m, with a 5.6% deviation from the simulation. Additionally, both yaw and roll angles were significantly reduced, improving vehicle stability during braking. Experimental data confirmed that the system dynamically maintains an optimal pressure difference of approximately 1.6 MPa between the front and rear axles, effectively preventing rear wheel lock-up and ensuring stable braking performance. The research demonstrates that this PID-based brake-by-wire distribution strategy significantly enhances both braking efficiency and driving stability, providing valuable insights for the development of high-performance electric vehicles.

Authors

Institutions

Publication Details

Journal
World Electric Vehicle Journal
Published
2025-11-28
DOI
https://doi.org/10.3390/wevj16120653
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Braking Force Distribution Strategy for Race Cars Based on PID Algorithm

Yiwen Pan, Yingbo Hua, Zhicheng Zhou, Jigang Liu
World Electric Vehicle Journal
Vehicle Dynamics and Control Systems
article

Research on Braking Force Distribution Strategy for Race Cars Based on PID Algorithm

Yiwen Pan, Yingbo Hua, Zhicheng Zhou, Jigang Liu
article en

Abstract

This study proposes a dynamic braking force distribution strategy based on a PID algorithm for Formula Student electric racing cars, addressing the limitations of fixed-ratio distribution methods in adapting to dynamic braking conditions. The strategy utilizes a PID controller targeting the desired slip ratio to dynamically adjust the braking force distribution coefficient (β) between the front and rear axles. The proposed method was validated through co-simulation using CarSim and Simulink, as well as real vehicle testing. Simulation results show a 7.7% reduction in braking distance under emergency braking at 100 km/h with the PID control strategy, while real vehicle testing confirmed a braking distance of 30 m, with a 5.6% deviation from the simulation. Additionally, both yaw and roll angles were significantly reduced, improving vehicle stability during braking. Experimental data confirmed that the system dynamically maintains an optimal pressure difference of approximately 1.6 MPa between the front and rear axles, effectively preventing rear wheel lock-up and ensuring stable braking performance. The research demonstrates that this PID-based brake-by-wire distribution strategy significantly enhances both braking efficiency and driving stability, providing valuable insights for the development of high-performance electric vehicles.

World Electric Vehicle JournalVol. 16(12)
Yancheng Institute of Technology (CN)
Yancheng Institute of Technology
Openalex Percentile: Top 58%
Vehicle Dynamics and Control Systems
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.