Electrification and automatization may change the environmental impact of vehicles. Current eco-driving approaches for electric vehicles fit the electric power of the motor by quadratic functions and are limited to powertrains with one motor and single-speed transmission or use computationally expensive algorithms. This paper proposes an online nonlinear algorithm, which handles the non-convex power demand of electric motors. Therefore, this algorithm allows the simultaneous optimization of speed profile and powertrain operation for electric vehicles with multiple motors and multiple gears. We compare different powertrain topologies in a free-flow scenario and a car-following scenario. Dynamic Programming validates the proposed algorithm. Optimal speed profiles alter for different powertrain topologies. Powertrains with multiple gears and motors require less energy during eco-driving. Furthermore, the powertrain-dependent correlations between jerk restriction and energy consumption are shown.

The standardized driving cycles, which are used around the globe for the development and homologation of automobiles, consist of a series of speed points versus time, to represent typical driving conditions and to exclude the influence of a human driver. However, with respect to autonomous vehicles (AVs), the driving style is defined in driving algorithms as a characteristic of the vehicle. Therefore, driving style should be considered in driving cycles. In this research, using MATLAB/Simulink^® we developed the AVDC (Autonomous Vehicle Driving Cycle) Tool, which is capable of generating driving cycles based on driving style characteristics. The autonomous vehicles being investigated drive in a simulated environment along a straight road amongst other traffic vehicles, applying standard cycles to ensure the representativeness of generated autonomous cycles. The autonomous vehicle is piloted by adaptive cruise control (ACC) for car-following and free driving. Overtake logic decides whether passing will be attempted. Driving style is defined by four aspects—comfort, safety, swiftness, and economy—and determines the control parameters in the driving algorithm. The driving cycles generated by the AVDC Tool for a variety of driving styles show diverse characteristics, thus indicating the effective representation of various driving styles.

State of the art powertrain optimization compares the energy consumption of different powertrain configurations based on simulations with fixed driving cycles. However, this approach might not be applicable to future vehicles, since speed advisory systems and automated driving functions offer the potential to adapt the speed profile to minimize energy consumption. This study aims to investigate the potential of powertrain optimization with respect to energy consumption under optimal energy-efficient driving for electric buses. The optimal powertrain configurations of the buses under energy-efficient driving and their respective energy consumptions are obtained using powertrain-specific optimized driving cycles and compared with those of human-driven unconnected buses and buses with non-powertrain-specific optimal speed profiles. Based on the results, new trends in the powertrain design of vehicles under energy-efficient driving are derived. The optimized driving cycles are calculated using a dynamic programming approach. The evaluations were based on the fact that the buses under energy-efficient driving operate in dedicated lanes with vehicle-to-infrastructure (V2I) communication while the unconnected buses operate in mixed traffic. The results indicate that deviating from the optimal powertrain configuration does not have a significant effect on energy consumption for optimized speed profiles; however, the energy savings from an optimized powertrain configuration can be significant when ride comfort is considered. The connected buses under energy-efficient driving operating in dedicated lanes may reduce energy consumption by up to 27% compared to human-driven unconnected buses.

Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source.

The consideration of the thermal behavior of electric machines is becoming increasingly important in the machine design for electric vehicles due to the adaptation to more dynamic operating points compared to stationary applications. Whereas, the dependency of machine efficiency on thermal behavior is caused due to the impact of temperature on the resulting loss types. This leads to a shift of efficiency areas in the efficiency diagram of electric machines and has a significant impact on the maximum load capability and an impact on the cycle efficiency during operation, resulting in a reduction in the overall range of the electric vehicle. Therefore, this article aims at analyzing the thermal load limits of induction machines in regard to actual operation using measured driving data of battery electric vehicles. For this, a thermal model is implemented using MATLAB^® and investigations to the sensitivity of model parameters as well as analysis of the continuous load capacity, thermal load and efficiency in driving cycles under changing boundary conditions are conducted.