The combination of the electric drive and the internal combustion engine (ICE) in hybrid electric vehicles (HEV) requires the implementation of an Energy Management Strategy (EMS). The task of the EMS is to split the driving demand between the two energy converters. The design of the EMS in charge-sustaining operation is commonly targeted at the minimization of fuel consumption. For in-vehicle implementation of the EMS, supplementary objectives, such as the electric driving (ED) experience or the driving comfort, influenced by the frequency of state shifts, are considered. Therefore, this work extends the framework for EMS optimization from the fuel-optimal design to multi-objective target spaces. First, the general multi-objective optimal control problem (MOOCP) is formulated. In a next step the central target space for EMS calibration consisting of fuel consumption, ED time and number of ICE starts is considered and the resulting MOOCP is solved using Dynamic Programming (DP). The solutions are compared in WLTC, and the resulting EMS behavior is analyzed. In the field of in-vehicle calibration of EMS, it is of fundamental importance to develop a basic understanding of the sensitivities between the optimization objectives. Therefore, the pareto-optimal solutions for several real-world driving cycles (DC) are determined and their characteristics are investigated. Using a statistical evaluation, correlations between the sensitivity and DC properties are derived. To further explore the sensitivities in the multi-objective target space considered, the influence of different driving resistance (DR) is highlighted. This work contributes by examining the correlations in the multi-objective design of the EMS for HEVs as well as the determination of the influence of different boundary conditions. In future work, the multi-objective optimization framework can be used as a benchmark for the design of causal EMS utilized in production vehicles.

The objective of this research is a generally valid and modular methodology for developing a causal, fuel-optimized, model-free energy management strategy (EMS), in-cluding a gear shift strategy, for hybrid electric vehicles (HEVs) with multiple electric machines (EMs) and possibly multiple transmissions. Therefore, this paper presents a novel methodology for the optimization-based development of a causal, cascaded, map-based EMS for HEVs with multiple control variables (MCVs), based on related research on Pontryagin's minimum principle (PMP), equivalent consumption minimization strategy (ECMS), and map-based EMS approaches. The proposed methodology is applicable to any hybrid powertrain topology, as well as battery electric vehicles (BEVs) with multiple EMs and possibly multiple transmissions. The EMS is mathemat-ically described as an optimal control problem in order to compute optimal control maps (OCMs) based on multi-criteria optimization considering soft and hard constraints. The EMS is defined as a multi-stage decision-making process and is therefore implemented as a cascaded logic. This enables offline calculated OCMs to be manipulated by downstream additional sub-optimal rules for system and gear state transitions at each decision level. The EMS can be implemented as either a non-predictive or predictive strategy. To demonstrate the functionality of the proposed methodology, it is applied to a P24-HEV as an example, and simulation results are presented and discussed. Finally, recommendations for future work are provided.

This paper presents a method to compute an approximation of actual internal combustion efficiency which is real time capable. Further, the implementation of this efficiency prediction into a hybrid electric vehicle energy management strategy is shown. It enables the computation of the optimal torque split under any varying condition. This real time capable approach, validated on the engine test bench, allows efficient operation of hybrid electric vehicles under real driving conditions. Research results show that, especially under conditions relevant for engine-knock, the potential fuel reduction is up to 10 % compared to actual known operating strategies exists.

The calibration of fuel-efficient energy management strategies (EMSs) for plug-in hybrid electric vehicles (PHEVs) requires an in-depth understanding of the powertrain component’s operation mode and, in particular, its optimal interaction. The derivation of optimal EMSs under stationary operating conditions and its application to the design process of rule-based operating strategies has thoroughly been researched in the past. However, when considering the warm-up period at cold ambient temperatures, the interaction becomes more complex and strongly influenced by transient component characteristics. The efficiency of pure electric driving (ED) and engine load point shifting (LPS) changes within the warm-up period and is even more affected by the respective driving route. Looking at the cabin heating demand suggests the possibility of an EMS being predictive and completely divergent at a first glance in order to provide a sufficient amount of waste heat for cabin heating purposes. This paper applies a forward dynamic programming (FDP) approach in order to focus on the impact of the transient internal combustion engine (ICE) temperature on an optimal operating strategy for hybrid electric vehicles (HEVs). Thresholds of efficient electric driving and engine characteristics with respect to optimal load point shifting are analyzed. Additionally, a set of representative driving routes is studied in order to gain further insights into the fuel saving potentials of predictive energy and thermal management strategies.

To reduce the local CO2 emission of vehicles, alternative zero emission propulsions systems can be used. The most prominent being battery electric vehicles. But the two major drawbacks of long refuelling times and comparably low driving ranges can be addressed by using a range extender.

Development and evaluation of energy management strategies (EMS) for hybrid electric vehicles (HEV) in early development phases is challenging. For precise analysis at this early stage, internal combustion engine (ICE) operation has to be reproduced as close as possible to its future in-vehicle use. This paper describes the development of an Engine-in-the-Loop (EIL) system as a tool for research of advanced HEV EMS.

The efficiency of internal combustion engines (ICEs) is well-known to be low at cold starting conditions. Additionally, the energy demand for passenger compartment heating is substantial and either provided by an electrical auxiliary heater or in terms of the engine waste heat. Plug-in hybrid electric vehicles (PHEVs) are prone to suffer from extended warm-up periods due to increased engine off periods as compared to conventional vehicles. Energy management strategies (EMSs) however allow for warm-up applications independent of the drive torque request. Optimal control theory serves as benchmark for EMS, offering a variety of numerical methods. This paper focuses on forward dynamic programming (FDP) accounting for causal system dynamics such as engine temperature. Results confirm, that even though the fuel saving potential of the engine warm-up is significant, it is hardly influenced by means of the EMS. A second implementation variant is proposed to counteract the curse of dimensionality. The presented approach not entirely achieves global optimality, yet yields results close to optimum within reasonable computational times. As opposed to the reference, the proposed method allows accounting for a variety of causal system dynamics leading towards an EMS more close to reality.

Fuels from lignocellulosic biomass have the potential to contribute to sustainable future mobility targets by reducing the fossil CO2 emissions of the transport sector. Of special interest for the diesel engine are oxygenated fuels, since they can help to solve traditional conflicts of objectives like the soot–NOx trade-off or the efficiency–NOx compromise. Dibutyl ether (DBE) and oxymethylene ethers (OME) are among the most promising fuel candidates. The suitability of these compounds for diesel engines is investigated in this study. The fuels are injected in pure form as well as a diesel–biofuel blend with 20% volumetric biogenic share. During the course of these investigations special attention is given to soot and particle emissions, and also to measured engine efficiency. The combustion tests are combined with an analysis of suitable production paths of the evaluated bio-ethers as second generation biofuels. Production simulation shows high greenhouse gas savings potential, but also high investment costs.