A significant disadvantage of battery electric vehicles compared to vehicles with internal combustion engines is their sharply decreased driving range at low temperatures. Two factors are primarily responsible for this decreased range. On the one hand, the energy demand of cabin heating needs to be supplied by the vehicle’s battery since less waste heat is available from the powertrain, which could be used to cover heating demands. On the other hand, a limited capability to recuperate at low temperatures serves to protect the battery from accelerated aging, which ultimately leads to less energy regeneration. This paper analyzes the impact of both factors separately on a battery electric vehicle’s driving range. Additionally, this paper provides technical requirements for the implementation of an electrothermal recuperation system. Such a system has the potential to reduce the impact of both abovementioned factors on driving range by enabling the direct usage of regeneratable energy for heating when battery charging is limited under cold conditions. The presented analysis is based on BMW i3 and Tesla Model 3 datasets, which combined cover more than 125 trips in and around Munich at different ambient conditions. The results show that the range can decrease by up to 31.9% due to heating and by up to 21.7% due to limited recuperation, which gives a combined maximum range decrease of approximately 50% under cold conditions. Additionally, it was found that a heater with a short reaction time in the lower millisecond range and a power capability of 20 kW would be sufficient for an electrothermal recuperation system to enable the utilization of unused regenerative braking potentials directly for heating.

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One of the decisive reasons for the slow market penetration of electric vehicles is their short driving range, especially in cold temperatures. The goal of this paper was to increase the driving range in cold temperatures. Electric vehicles recover kinetic energy by recuperation and storage in the battery. However, if the battery is fully charged or cold, the option of recuperation is severely limited. Braking energy is dissipated into the environment via the mechanical brake, and the range thus decreases. Electrothermal recuperation (ETR) enables the braking power to be used in heater systems and thus saves energy in the overall system. In this paper, ETR was investigated with a highly responsive serial layer heater. An overall model consisting of the electric powertrain, the heating circuit, and the vehicle interior was developed and validated. The limitations of recuperation capability were determined from driving tests. The factors state of charge and battery temperature were varied in the conducted simulations in order to quantify the range increase through ETR. The results showed that the range could be increased via electrothermal recuperation by up to 8% at −10 °C in a real driving cycle, using a serial heater. A control strategy of the heating circuit enabled the coolant circuit to function as buffer storage. The interior temperature—and consequently user comfort—remained unchanged.

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Energy management systems are used to find a compromise between conflicting goals that can be identified for battery electric vehicles. Typically, these are the powertrain efficiency, the comfort of the driver, the driving dynamics, and the component aging. This paper introduces an optimization-based holistic energy management system for a battery electric vehicle. The energy management system can adapt the vehicle velocity and the power used for cabin heating, in order to minimize the overall energy consumption, while keeping the total driving time and the cabin temperature within predefined limits. A genetic algorithm is implemented in this paper. The approach is applied to different driving cycles, which are optimized by dividing them into distinctive time frames. This approach is referred to as the sliding window approach. The optimization is conducted with two separate driving cycles, the New European Driving Cycle (NEDC) and a recorded real-world drive. These are analyzed with regard to the aspects relevant to the energy management system, and the optimization results for the two cycles are compared. The results presented in this paper demonstrate the feasibility of the sliding window approach. Moreover, they reveal the differences in fundamental parameters between the NEDC and the recorded drive and how they affect the optimization results. The optimization leads to an overall reduction in energy consumption of 3 . 37 % for the NEDC and 3 . 27 % for the recorded drive, without extending the travel time.