• Experimental comparison of CBSwith 3 configurations of EPB Systemdepending on number of EPRs used.

• Reduction in stopping distance wasquantified using TruckSim.

• Single EPR, two EPR and four EPR

brake systems lead to a stopping

distance reduction of at least 2.17 m

(3.19%), 8.18 m (12.05%) and 12.64

m (18.63%) relative to CBS.

.

• Conventional air brake system(CBS) Time lag Slowerresponse Longer stoppingdistance.

• Electro-Pneumatic Brake System(EPB) foot valve replaced with anElectro-Pneumatic Regulator(EPR).

Department of Engineering Design

OpenHouse 2014

Electro-Pneumatic Brake

System

Performance Evaluation

of EPB System

Hysteresis Modeling of the Brake Chamber

Publications

Automotive Control Systems Laboratory Dr. C. S. Shankar Ram

ddesLyapunov-based

Deceleration

Control

Lyapunov-based

Pressure

Control

Air Brake

System

Longitudinal

Dynamics Model+-

+-

Pdes dactPactV

• Correlation between voltage input toEPR and pressure transients in thebrake chamber.

• EPR dynamics, compressed air flowdynamics and dynamics of the brakechamber incorporated.

Modeling of EPB System

• E. Arun Kumar, Vikas Gautam and Shankar C. Subramanian, “Performance evaluation of an electro-pneumatic braking system for commercial vehicles,”Proceedings of the 2nd International Conference on Power, Control and Embedded Systems, Allahabad, India, pp. 406-411, 2012.

• Jatheendranath Moothayil, Srinidhi K. and Shankar C. Subramanian, “Hysteresis modeling of a pneumatic brake chamber in a heavy commercial vehicle,”Proceedings of the 3rd Joint International Conference on Multibody System Dynamics and the 7th Asian Conference on Multibody Dynamics, Busan,Korea, 2014.

Control of EPB System

• Brake system model integrated withvehicle longitudinal model to developLyapunov based deceleration andpressure control schemes.

• Hardware-in-loop experiments werecarried out to evaluate controllers.

The hysteretic behavior of pneumatic

brake is modeled using a modified Bouc-

Wen hysteresis model.

This helps to predict the push rod stroke

as a function of brake chamber pressure

during repeated application of brakes.

The parameters of the model are

identified using Genetic Algorithm from

experimental data.

Real Time Controller

ExperimentalBrake

SystemTruckSimSimulate Vehicle

Dynamics

NonlinearBrake

SystemModel

Host PC

Control Algorithm

Hardware in Loop Test Setup

Research Scholars : Vikas Gautam and Jatheendranath Moothayil

• Vignesh Rajaram and Shankar C. Subramanian, “A Model Based Collision Avoidance Algorithm for Heavy Commercial Vehicles,” Proceedings of the 2014

American Control Conference, Portland, Oregon, USA, pp. 3213-3218, June 2014.

• Vignesh Rajaram and Shankar C. Subramanian, “A model based rear end collision avoidance algorithm for heavy commercial road vehicles,” In Press, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.

Department of Engineering Design

OpenHouse 2014

Active Safety Systems Research Areas

Automotive Control Systems LaboratoryDr. C. S. Shankar Ram

Collision Warning and Collision Avoidance.

Roll-Over Prevention.

Yaw Stability.

Collision Warning and Collision Avoidance

An Advanced Driver Assistance System (ADAS) is an

active system that monitors the state of the vehicle

and assists the driver during emergencies.

Important Outcomes

Mathematical Model

• Vehicle Model

• Brake system Model

Controller Design

• Model Based Design

• Controller Tuning

Simulation and Testing

• Matlab/Simulink

• Vehicle Dynamics Simulation Software

Deployment

• HiL

• Scaled Model Prototype

Work Flow

Collision Controller

Electro

Pneumatic

Regulator

Brake

SubsystemPressure

ControllerSensorLongitudinal

Vehicle Model

Publications

• A full state feedback controller has been developed, considering

• Brake force distribution between the wheels.

• Maximum traction available.

• Road grade.

• Load transfer during braking.

• Brake dynamics.

• Testing using simulation and HiL are able to prevent the

collision by applying the brake and stop the vehicle within the

safe distance.

• The developed algorithm is able to work under different

• Road conditions (wet, dry, icy).

• Load conditions (fully laden, partly laden, unladen).

Host Vehicle Lead Vehicle

Research Scholar : Vignesh Rajaram

ds

Department of Engineering Design

OpenHouse 2014

Automotive Control Systems Laboratory Dr. C. S. Shankar Ram

Roll-Over Prevention (with Prof. R. Krishnakumar)

SteeringShock absorber

Air reservoir

EPR

MPU 6050

Arduino

Pneumatic actuator

DC

motor

• Prevention of un-tripped rollover using

pneumatic actuators.

• Rollover detection is based on Load

Transfer Ratio (LTR).

• Linear Quadratic Regulator is used for

control.

• Scaled model is developed based on

dimensional analysis.

Yaw Stability Control

Yaw Rate

Controller

Pressure

Controller

Electro-Pneumatic

Regulator

Brake

Chamber

Vehicle

Model

Steering

Angle

Pressure

Yaw rate, longitudinal velocity,

longitudinal and lateral acceleration

Brake Pressure Control

• Integrated controller design of

• Longitudinal stability by wheel slip control.

• Yaw stability by differential braking.

Control of Unmanned Ground Vehicles

• Model based control to minimize the

heading angle error between the vehicle and

the defined path for an Unmanned Ground

Vehicle (UGV).

• The novelty is to include the actuator

dynamics as the response time to steer the

front wheel is in the same order as that of

the heading angle dynamics of the vehicle.

• For a desired heading angle of 200 , the

maximum error in the heading angle

between the experimental and simulated

results are 9.10 and 3.50 without actuator

dynamics & considering actuator dynamics

respectively.

Sahoo, S., Subramanian, S. C. and Srivastava, S. (2012). “Design and implementation of a controller for navigating an autonomous ground vehicle”, Proceedings of the IEEE International Conference on Power, Control and Embedded Systems (ICPCES), Allahabad, India, pp. 247-252.

Sahoo, S., Subramanian, S. C., Mahale, N. and Srivastava, S., “Design and development of a heading angle controller for an unmanned ground vehicle”, In Press, International Journal of Automotive Technology.

Sahoo, S., Subramanian, S. C. and Srivastava, S., “Sensitivity analysis of vehicle parameters for heading angle control of an unmanned ground vehicle”, ASME 2014 International Mechanical Engineering Congress and Exposition (IMECE), Montreal, Canada, (Accepted).

Publications

Research Scholars : Nagendran Manisekaran, Jatheendranath Moothayil and Shubhashisa Sahoo

• C S Nandakumar and Shankar C. Subramanian, “Design and analysis of a series hybrid electric vehicle for Indian conditions,” Proceedings of the ASME 2012 International Mechanical Engineering Congress & Exposition (IMECE2012), November 9-15, 2012, Houston, Texas, USA.

Department of Engineering Design

Open House 2014

Hybrid Electric Vehicle Research Focus

Performance Analysis of a Series Hybrid Electric Vehicle Dr. C. S. Shankar Ram

Vehicle performance(acceleration and braking) to analyze energy consumption and potential for energy saving for a modified Indian drive cycle.

Energy regeneration using combined braking strategies.

Energy management strategies - control of power distribution from power plants under different operating modes for optimization of engine and EM.

Series HEV Drive System

A vehicle that combines the features of a conventional ICE

propulsion system and an electric propulsion system to achieve

better fuel economy and performance.

Important Outcomes

Publications

Vehicle

controller

Motor

controller

Electric

motor

Engine Generator

High voltage

battery pack

Generator

controller

Engine

controller

High voltage

Control signal

Brake demand

Battery

controller

Vehicle speed

Driver’s Brake demand

Start

Low

Braking force

demand

Battery

SOC

Vehicle

velocity

Yes

No Only mechanical

friction brake at all

wheels

Brake

demand

< motor

rating

Regenerative brake at

rear axle and friction

brake at all wheels

No

Only Regenerative

brake at rear axle

Yes

Return

Deceleration (g)

Bra

kin

g fo

rce

Regenerative brake (Rear axle)

Bra

kin

g fo

rce

Brake pedal travel O

Regenerativebrake at rear axle

Deceleration (g)

Bra

king

forc

e

Regenerative Braking Strategy

Energy Regeneration (@100% motor braking power)

• Combined braking strategy0.2 kWh ( 77% of the total braking energy)

• Parallel braking0.06 kWh (23% of the total braking energy)

Combined braking is 1.5 time efficient than parallel braking.

Due to limitation in the lead acid battery chargingrate, regenerative braking current is limited to 100 Ain testing. Hence, the motor braking power islimited to 30% of the estimated rating (4.95 kW).

Total Energy consumption

(kWh)

Energy efficiency

(%)Friction brake 4.2 0Parallel brake 3.5 16%Combined brake 3.1 23%

Brake Performance Test

0.026

0.019

0.027

0.009

0.0040.005

0.014

0.008

0.011

0.000

0.005

0.010

0.015

0.020

0.025

0.030

40 to 10 30 to 10 35 to 5

En

erg

y (k

Wh

)

Braking speed (kmph)

Total braking energy Parallel braking energy

Combined braking energy

% of Motor braking power

Equivalent motor braking

power (kW)

Braking energy regeneration

Parallel Combined

100% 16.5 23% 77%

50% 8.25 21% 56%

25% 4.125 18% 33%

Energy consumption across urban cycle

Research Scholars : C S Nandakumar and Swagata Borthakur