This work investigated the effect of a Nu Vinci type Continuous Variable Transmission (CVT) based Energy Efficient Cruise Control (EECC) on an Electric Vehicle's (EV's) energy consumption. Unlike petrol and diesel vehicles, almost all EVs have a fixed gear ratio. Although it reduces the capital cost, it may not offer optimal energy efficiency and may increase the recharge costs. While accelerating and cruising, it may cause an EV to spend more energy than needed. While braking, it may cause underutilisation of the regenerative braking potential. In this work, an EECC was designed to operate the EV's power train close to its peak efficiency region by controlling the CVT ratio and Electric Machine (EM) torque. The EECC exploits data from the lead vehicle and employs a constrained linear Model Predictive Control (MPC) with a novel problem formulation that reduces the computational complexity. The proposed EECC was tested in a MATLAB simulation environment for different drive cycles. The results show that compared to a baseline EV with a fixed gear ratio and an Adaptive Cruise Control, the proposed system can reduce an EV's energy consumption in urban drive cycles by 16.6%.

Effect of a Nu Vinci type CVT based Energy Efficient Cruise Control on an Electric Vehicle's Energy Consumption

Corno M.
2022-01-01

Abstract

This work investigated the effect of a Nu Vinci type Continuous Variable Transmission (CVT) based Energy Efficient Cruise Control (EECC) on an Electric Vehicle's (EV's) energy consumption. Unlike petrol and diesel vehicles, almost all EVs have a fixed gear ratio. Although it reduces the capital cost, it may not offer optimal energy efficiency and may increase the recharge costs. While accelerating and cruising, it may cause an EV to spend more energy than needed. While braking, it may cause underutilisation of the regenerative braking potential. In this work, an EECC was designed to operate the EV's power train close to its peak efficiency region by controlling the CVT ratio and Electric Machine (EM) torque. The EECC exploits data from the lead vehicle and employs a constrained linear Model Predictive Control (MPC) with a novel problem formulation that reduces the computational complexity. The proposed EECC was tested in a MATLAB simulation environment for different drive cycles. The results show that compared to a baseline EV with a fixed gear ratio and an Adaptive Cruise Control, the proposed system can reduce an EV's energy consumption in urban drive cycles by 16.6%.
2022
2022 European Control Conference, ECC 2022
978-3-9071-4407-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1233601
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