Novel Energy Management Strategy for Electric Vehicles to Improve Driving Range

This study aims to minimize the losses of an Electric Vehicle (EV) and improve its driving range by introducing a multidimensional Energy Management Strategy (EMS). The exact behavior of an Interior Permanent Magnet Synchronous Motor (IPMSM) is studied through various experimental tests. An efficiency map and a power loss map are derived by calculating the losses of motor-inverter system in harmonics and fundamental frequency. An Artificial Neural Network (ANN) is trained to model the motor-inverter system. The required kinetic energy on the axle to move the vehicle is estimated using the longitudinal model of the vehicle. A Genetic Algorithm (GA) is set to find the optimum operational conditions that require lower force on the axle and guarantees lower power loss. Several tests and benchmarks are conducted on each subsystem to validate their effectiveness. The resultant is an EMS that optimizes the speed profile, and reduces consumption by 12 to 17 percent when contrasted against four internationally recognized driving cycles.

Automated summary

This study introduces a multidimensional Energy Management Strategy (EMS) to minimize losses and improve the driving range of an Electric Vehicle (EV). The behavior of an Interior Permanent Magnet Synchronous Motor (IPMSM) is studied through experimental tests, resulting in the derivation of an efficiency map and a power loss map. The motor-inverter system is modeled using an Artificial Neural Network (ANN), and a Genetic Algorithm (GA) is used to find optimum operational conditions. Validation tests and benchmarks are conducted on each subsystem, resulting in an EMS that optimizes speed profile and reduces consumption by 12 to 17 percent compared to recognized driving cycles.