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Performance and Comfort Optimization from ABS/CBS/Motor Regenerative Braking in an Electric Two Wheeler during Heavy and Mild Braking Respectively
ISSN: 0148-7191, e-ISSN: 2688-3627
Published January 09, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Reducing the number of traffic accidents is a declared target of most governments leading to mandating Combined Braking System (CBS) or Anti-lock Braking System (ABS) in two wheelers.
Traditional friction braking torque and motor braking torque can be used in braking for electric 2wheeler. Use of CBS and ABS helps in active control of vehicle braking leading to better deceleration, prevention of tire locking, control on vehicle pitch etc.
A braking model (friction braking + motor regenerative braking) along with battery dynamics is developed in Matlab/Simulink and validated with real vehicle response.
This paper presents an analysis on vehicle braking separately for heavy and mild braking in various vehicle load conditions. During heavy braking a feedback control algorithm is used to maintain optimal slip ratio both at the front and rear tire, active control on CBS and ABS+Regen is done and performance is compared. Similarly, during mild braking rider’s comfort is addressed by optimizing the vehicle pitch and pitch-rate, this gives an analysis/control on rider comfort. Model based simulation helps in precise analysis of corner cases like slip-ratio, suspension bump-stop etc. and gives an overall objective analysis on system design.
|Ground Vehicle Standard||Antilock Brake System Review|
|Technical Paper||An Experimental and Theoretical Study on the Vehicle Brake Judder|
|Technical Paper||The Compatibility of Air Disc Brakes and S-cam Brakes Installed on Combination Vehicles|
CitationSoni, L., Venkateswaran, S., and Ramachandran, V., "Performance and Comfort Optimization from ABS/CBS/Motor Regenerative Braking in an Electric Two Wheeler during Heavy and Mild Braking Respectively," SAE Technical Paper 2019-26-0122, 2019, https://doi.org/10.4271/2019-26-0122.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
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