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Automatic Torque Mode Selection Based on Vehicle Load for Commercial Electric Vehicles
Technical Paper
2021-26-0141
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Range of an electric vehicle is one of the most prominent decisive factors for a person willing to buy an electric vehicle. In this paper an algorithm is developed to estimate the load carried by the truck or passengers in case of a bus and accordingly switch between ECO, ECO+, Normal, Power, and Power+ modes. This is similar to the ECO/Power switches available in the vehicles, but here auto switching is done to reduce driver dependability and allow vehicle to operate in 5 different modes without driver intervention. Optimum utilization of available torque is done for efficient operation of the vehicle in all load and road conditions. The model-based software development using MATLAB Simulink is used to develop an algorithm which will switch to Power or Power+ torque mode if the vehicle is fully laden or if the vehicle is going on a steep hill, whereas the algorithm will switch to ECO+ or ECO mode if the vehicle is empty or carrying less load. These torque mode shifts can occur multiple times in a drive cycle and the switching is gradual so that there is no abrupt change in the vehicle torque or speed. Trials with this technique have been carried out and achieved more than 10% range improvement in a 5-ton GVW light duty electric truck. Buses carrying passengers also do not run on full capacity all the time, this algorithm manages the vehicle torque to adjust to the load on the vehicle and improving driving range. The algorithm also reduces peak currents drawn by the motor from the battery which keeps the cell temperatures under control and improves battery life. Even a 10% improvement in the range of a commercial electric vehicle gives promising figures for the energy saving in the life-cycle of a vehicle.
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Nesamani, K., Pandey, R., and Jain, R., "Automatic Torque Mode Selection Based on Vehicle Load for Commercial Electric Vehicles," SAE Technical Paper 2021-26-0141, 2021, https://doi.org/10.4271/2021-26-0141.Data Sets - Support Documents
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References
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