The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced, based on the combination of Rule-Based and Equivalent Consumption Minimization Strategies (RB-ECMS). The RB-ECMS uses only one decision variable and requires no tuning of many threshold control values and parameters. This decision variable represents the maximum propulsion power of the secondary power source (i.e. electric machine/battery) during pure electric driving. The RB-ECMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB-ECMS proposed in this paper requires significantly less computation time with a similar result to DP (within 1% accuracy).

This paper proposes a design method for the energy management strategy to explore the potential fuel saving of a hybrid electric vehicle (HEV) equipped with an automated manual transmission. The control algorithm is developed based on the combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP) to optimally control the discrete gearshift command, in addition to the continuous power split between the internal combustion engine and the electric machine. The proposed method outperforms DP in terms of computational efficiency, being 171 times faster, without loss of accuracy. Simulation results for a middle-sized HEV on the New European Drive Cycle show that, to further optimize the gearshift strategy, an additional fuel saving of 20.3% can be reached. Furthermore, with the start-stop functionality available, it is shown that the two-point boundary-value problem following from PMP cannot be solved with sufficient accuracy without loss of optimality. This means that the finding of a constant value for the Lagrange multiplier while satisfying the battery state-of-energy (SOE) at the terminal time is not always guaranteed. Therefore, an alternative approach of SOE feedback control to adapt the Lagrange multiplier is adopted. The obtained results are very close to the globally optimal solution from DP. Simulation results, including the start-stop functionality, show that the relative fuel saving can be up to 26.8% compared with the case of a standard gearshift strategy.

Electric vehicles are seen as the most promising solution to convert sustainable energy into drive energy. However, there are still some major (technological) challenges, e.g., in terms of maximizing range anxiety, minimizing battery costs and charging time. A possible solution, in order to improve the relative limited range (100-200 km), battery life time and ultimate costs, is utilization, e.g., of a transmission technology between the electric machine and driven wheels. The main research question of this paper is, what are the effects of transmission types (fixed, manual, continuously variable) and parameters (final drive ratio, efficiency) on the overall (battery-to-wheel) efficiency and performance of an electric vehicle? A backwards-simulation model from the wheels (load) to the batteries (source) has been developed to simulate an electric vehicle on a representative drive cycle (NEDC, FTP75). The model incorporates rotating (drive train) inertias, different transmission types, regenerative braking, battery efficiency and a shift strategy optimization algorithm based on Dynamic Programming (DP). The results show that with an optimized shifting strategy compared to a prescribed strategy using a Manual Transmission (MT) a maximum energy saving of 6% is possible. The conventional push-belt Continuously Variable Transmission (CVT) is less energy efficient and needs more battery energy for driving compared to a fixed-gear transmission type. Mainly, caused by the typically lower CVT efficiency. Further, it was found that reducing the final drive ratio improves the overall efficiency. The range in which the electric motor operates was shifted to higher angular velocity resulting in higher efficiency operating points. If the transmission efficiency of all the transmission types are equal assumed, then the CVT is typically the most energy efficient transmission type. For example, an energy saving of 7% is calculated compared to a well-selected fixed-gear ratio transmission type.

Currently, many different topologies are designed with different transmission technologies such as automated manual transmission (AMT) and continuously variable transmission (CVT). The choice of topology determines the energy-flow efficiency between the hybrid system, the engine, and the vehicle wheels. The optimal topology minimizing fuel consumption is influenced by the transmission technology. Therefore, an AMT (high efficiency) and a push-belt CVT (moderate efficiency), are used in this research for comparison. In addition, a controlled switching topology is introduced as a benchmark, where controlled coupling with additional clutches of the electric machine before or after the transmission minimizing transmission losses and improving hybrid performance is investigated. The results showed that a switching topology can significantly improve CO₂ emission reduction (average relative improvements between 2% and 7%), particularly for CVT-based hybrid vehicles. Moreover, in case of an AMT, a precoupled topology is preferable, and in the case of a CVT, a postcoupled is preferable for full-hybrid vehicles. For these cases, selecting the optimal fixed topology can improve the relative CO₂ emission reduction between 2% and 8%.

Powertrain system design optimization is an unexplored territory for battery electric trucks, which only recently have been seen as a feasible solution for sustainable road transport. To investigate the potential of these vehicles, in this paper, a variety of new battery electric powertrain topologies for heavy-duty trucks is studied. Thereby, topological design considerations are analyzed related to having: (a) a central or distributed drive system (individually-driven wheels); (b) a single or a multi-speed gearbox; and finally, (c) a single or multiple electric machines. For reasons of comparison, each concurrent powertrain topology is optimized using a bilevel optimization framework, incorporating both powertrain components and control design. The results show that the combined choice of powertrain topology and number of gears in the gearbox can result in a 5.6% total-cost-of-ownership variation of the vehicle and can, significantly, influence the optimal sizing of the electric machine(s). The lowest total-cost-of-ownership is achieved by a distributed topology with two electric machines and two two-speed gearboxes. Furthermore, results show that the largest average reduction in total-cost-of-ownership is achieved by choosing a distributed drive over a central drive topology (−1.0%); followed by using a two-speed gearbox over a single speed (−0.6%); and lastly, by using two electric machines over using one for the central drive topologies (−0.3%).

Lap time is the most relevant performance indicator in electric racing. In order to minimize it, it is important to carefully design the powertrain of the race car in terms of sizing and technology, and meticulously administer the energy available on-board. To facilitate efficient design, this paper presents a convex optimization framework to rapidly compute the minimum-lap-time control strategies for a battery electric race car. Specifically, we first identify a convex model of the electric powertrain, including the battery, the electric motor, and two transmission technologies: a fixed-gear transmission (FGT) and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the minimum-lap-time control problem in a convex fashion for a given driving path, and compute its globally optimal solution with second-order conic programming algorithms. Third, we showcase our framework on the Le Mans track by comparing the performance achievable with an FGT and a CVT, and validate it with nonlinear simulations. Our results show that for the given setup a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, significantly outperforming the lap time achievable with an FGT. Finally, we leverage the computational efficiency of our framework to carry out parameter studies on the components, revealing that optimizing the size of the battery and the motor for the specific scenario can considerably improve the achievable lap time, and that the best transmission strongly depends on the sizing decisions.