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This paper compares the impact of two scaling methods of electric machines on the energy consumption of electric vehicles. The first one is the linear losses-to-power scaling method of efficiency maps, which is widely used in powertrain design studies. While the second is the geometric scaling method. Linear scaling assumes that the losses of a reference machine are linearly scaled according to the new desired power rating. This assumption is questionable and yet its impact on the energy consumption of electric vehicles remains unknown. Geometric scaling enables rapid and accurate recalculation of the parameters of the scaled machines based on scaling laws validated by finite element analysis. For this comparison, a reference machine design of 80 kW is downscaled with a power scaling factor of 0.58 and upscaled considering a power scaling of 1.96. For comparative purposes, optimal combinations of geometric scaling factors are determined. The scaled machines are derived to fit the driving requirements of two electric vehicles, namely a light-duty vehicle and a medium-duty truck. The comparison is performed for 9 standardized driving cycles. The results show that the maximal relative difference between linear and geometric scaling in terms of energy consumption is 3.5% for the case of the light-duty vehicle, compared with 1.2% for the case of the truck. The findings of this work provide evidence that linear scaling can continue to be used in system-level design studies with a relatively low impact on energy consumption. This is of high interest considering the simplicity of linear scaling and its potential for time-saving in the early development phases of electric vehicles

The series–parallel architecture is the most interesting for hybrid electric vehicles, allowing the lowest fuel consumption. Unlike passenger cars, this architecture is not commercially available on the heavy-duty vehicles market. This is due to technical limitations associated with unsufficient load capability of the geartrain. To address this issue, new transmissions, such as the electrical variable transmission, have been developed. The novelty of this paper relies on the hybridization of a long-haul truck using the electrical variable transmission. This study aims to investigate the potential of using this new transmission for trucks. For that aim, fuel consumption benchmarking of three powertrain topologies is performed, considering: (a) a gearless topology; (b) a geared topology that uses one gearbox inserted between the engine and the mechanical input port of the electrical variable transmission; (c) a geared topology similar to the second one, but, with an additional multi-stage gearbox inserted to the mechanical output port of the electrical variable transmission. For a fair comparison between the different topologies, a bi-level optimization process has been used, incorporating the optimization of both components sizing and control. Results show that the fuel consumption of the gearless powertrain is higher than the engine-powered truck due to higher losses in the electrical variable transmission. While maximum fuel reduction of 14.2% was obtained by a geared topology that uses two gearboxes. Furthermore, emphasis is given to understand the effect of the powertrain component sizing on fuel consumption. Depending on the defined sizing, a possible fuel reduction is achieved from 3.3% to 14.2% for the two geared topologies. The reduction of CO2 emissions is found to be proportional to the fuel savings. Considering a long-haul mission, the last findings prove that the electrical variable transmission exhibits potential to reduce fuel consumption, if an adequate powertrain topology and its sizing are well defined.

The paper presents a method for structuring a scalable model of a permanent magnet synchronous machine in a manner that facilitates its integration into system-level simulations. This is achieved by utilizing the energetic macroscopic representation formalism to organize the equations of the scaling laws. The model comprises a fixed reference permanent magnet synchronous machine model that is complemented with two electrical and mechanical power adaptation elements to ensure scalability. Three scaling choices are analyzed, and the findings reveal that the equations for the power adaptation elements differ based on the selected scaling choice.