Ride Mode Development from Motor Characteristics and Riding Data Collection Simple Energy Pvt Limited

Format:

Book

Conference/Event

Author/Creator:

Gautam, Ashish, author.

Contributor:

Kamble, Dipak

Soni, Lokesh

Subramani, umashankar

Conference Name:

10TH SAE India International Mobility Conference (2022-10-12 : Bangalore, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

Electric Vehicles provide a sustainable transportation alternative for the future due to zero carbon emissions during running, higher efficiency, and lower operating cost along with various other benefits. One of the key challenges in the adoption of Electric Vehicles is to improve the range and reduce the anxiety associated with it, at the same time provide good drivability characteristics. A good tuning of riding mode is very crucial to make sure a good range is achieved without making any unwanted compromise on drivability.In this paper we try to elaborate on the methodology to be used to provide a good throttle response, for which it is very important to understand the torque requirement by the customer in different scenarios encountered during his/her day-to-day riding. For this purpose, various user drive cycles are imported in a MATLAB-Simulink model, where it was analyzed to provide the density map of the Torque Requirements v/s RPM for Motor to meet the ride pattern. Motor also has an inherent efficiency performance w.r.t the operating conditions such as Operating Voltage, RPM, Temperature, Physical Characteristics et cetera When the both the data sets id est Torque Requirements v/s RPM from the drive cycles and Motor efficiency in different Torque RPM Map are overlapped, we are able to identify the areas to be focused and list down the best overlapping zones between the two in decreasing order of preference. Through this a decision matrix is made to filter out the desired torque-rpm graph for Eco-mode. A simulation model of vehicle is correlated with real vehicle data to gain confidence on the analysis, and the resultant (eco-mode) was implemented on real vehicle

Notes:

Vendor supplied data

Publisher Number:

2022-28-0055

Access Restriction:

Restricted for use by site license