Energy management for automotive power nets

Reducing fuel consumption has always been a major challenge to the automotive industry. Whereas first marketing aspects gave rise to innovative research, today the environmental regulations have become the main driving force behind new technologies. Historically, the research concentrated on improvements for the mechanical side of the vehicle. However, the introduction of Hybrid Electric Vehicles (HEV), where the propulsion power can also be delivered by an electric machine, definitely emphasizes the benefits of electro-mechanical solutions. With a secondary power source, the HEV can satisfy the vehicle power demand in various ways. An energy management (EM) strategy is needed to control this added freedom in a fuel-efficient way. At present, a broad range of EM strategies has been proposed in literature and several concepts have been implemented in series-production vehicles. Typically, the academic solutions focus on complex optimization techniques, arising from well defined mathematical problems. The engineering approach offers a sub-optimal strategy, based on heuristic rules. Nevertheless, both policies fail when the important vehicle characteristics for EM are not well understood. The main contribution of this thesis is to deduce a physical explanation of the EM problem for all HEV configurations, viz., the series-HEV, the parallel-HEV and the combined series/parallel-HEV. By having a good understanding of the vehicle properties of interest, it becomes possible to develop a model-based EM strategy that mimics the optimal solution, without the need for complex optimization routines, nor the necessity for having accurate predictions about the future driving cycle. The proposed causal strategy is directly suitable for on-line implementation in a vehicle. The primary goal of an EM strategy is to maximize the fuel efficiency of the vehicle. In practice, this requirement is often associated with operating the internal combustion engine (ICE) in its highest efficiency region. Nevertheless, this thesis reveals that this concept is only partially true. A better understanding of how to operate the ICE follows from two other properties: the slope of the fuel map and its fuel offset at idle speed. A formal optimization problem is formulated to prove that these properties also relate to a mathematical interpretation, and infer from the optimal solution. For all the HEV configurations mentioned above, a power-oriented vehicle model is derived. Next, a suitable EM strategy is proposed. This strategy originates from a non-causal global optimization, but through a physical understanding of the parameters of interest, it is translated into a causal on-line strategy. To cope with uncertainties in the future power demand, a feedback mechanism is added which automatically regulates the energy in the battery near a reference value. Contrary to standard control experience, this feedback control loop has a better performance if it incorporates a small bandwidth and a large tracking error. Simulation results for all HEVs demonstrate that the proposed EM strategy achieves a fuel economy which is almost equivalent to the optimal solution. Moreover, when the fuel costs for producing electric power are accurately known in advance, this strategy has the ability to further improve its performance. In practice, however, this requirement is inappropriate, since causality of the EM strategy is lost. An alternative methodology is presented to include road predictions into the causal EM strategy. By means of an electronic horizon, the prediction information is translated into a preferred reference trajectory for the energy stored in the battery. However, it will be demonstrated that the added value of having knowledge about the future driving cycle is limited, compared to the situation without prediction information. Finally, the EM concept can also be applied to the electric power net of vehicles with a traditional drive train, or micro HEVs. Here, the alternator takes the position of the electric machine. As a case-study, the EM strategy has been implemented in a Ford Mondeo vehicle. Vehicle experiments on a roller-dynamometer test-bench show that profits in fuel economy are achieved up to 2.6% for a typical driving cycle. Although the potential fuel benefits are limited for the vehicle under consideration, the return on investment is extremely high, since it requires primarily changes in the vehicle software

Towards integrated powertrain control: thermal management of NG heated catalyst system

Towards Integrated Powertrain Control: Thermal Management of NG Heated Catalyst System — The conversion efficiency of catalytic converters is mainly defined by the temperature range wherein they are operating. Traditionally, ignition retard has been used to reduce the light-off time of the catalyst. This is however associated with a fuel penalty. With increasing vehicle electrification, external heating facilities present an alternative, especially for hybrid vehicles. Nevertheless, system complexity of hybrid vehicles prevents engineers to evaluate possible heating technologies with respect to traditional solutions

On-line identification of vehicle fuel consumption for energy and emission management : an LTP system analysis

An Energy Management (EM) system traditionally relies on (quasi) static maps offering efficiency parameters of the vehicle powertrain. During a vehicle's life span, these maps lose validity, so optimal performance for EM is not assured. This paper presents a proof-of-concept for a novel measurement system, estimating important engine and generator characteristics on-line during driving. The generator applies a small excitation signal to the combustion engine and by means of correlation techniques and feedback control, the incremental fuel cost for generating electric power is estimated. This information is very relevant for EM in Hybrid Electric Vehicles. No additional sensors (e.g. torque estimators) are needed. Under mild assumptions it is shown that the measurement system satisfies a Linear Time Periodic (LTP) System. Harmonic analysis as well as Floquet Theory are used to analyze performance and stability criteria. Simulation results support this analysis and demonstrate good noise rejection of the system

On-line identification of vehicle fuel consumption for energy and emission management: An LTP system analysis

An Energy Management (EM) system traditionally relies on (quasi) static maps offering efficiency parameters of the vehicle powertrain. During a vehicle's life span, these maps lose validity, so optimal performance for EM is not assured. This paper presents a proof-of-concept for a novel measurement system, estimating important engine and generator characteristics on-line during driving. The generator applies a small excitation signal to the combustion engine and by means of correlation techniques and feedback control, the incremental fuel cost for generating electric power is estimated. This information is very relevant for EM in Hybrid Electric Vehicles. No additional sensors (e.g. torque estimators) are needed. Under mild assumptions it is shown that the measurement system satisfies a Linear Time Periodic (LTP) System. Harmonic analysis as well as Floquet Theory are used to analyze performance and stability criteria. Simulation results support this analysis and demonstrate good noise rejection of the system

Plug-in hybrid electric vehicles in dynamical energy markets

The plug-in hybrid electric vehicle allows vehicle propulsion from multiple internal power sources. Electric energy from the grid can be utilized by means of the plug-in connection. An on-line energy management (EM) strategy is proposed to minimize the costs for taking energy from each power source. Especially in a dynamical energy market, an on-line optimization algorithm is desirable since energy prices change over time. By construction, the proposed EM system can operate with, and without prediction information. If predictions are available, an electronic horizon is applied to anticipate on up-coming events and further optimize the strategy. Illustrative examples are given to explain the added value for both solutions. Also the situation where energy is transferred back to the grid is considered. © 2008 IEEE

Integrated Powertrain Control for optimizing CO2-NOx emission trad-eoff in heavy-duty hybrid electric vehicles

Energy management in modern vehicles typically relates to optimizing the powerflow in the (hybrid) powertrain, whereas emission management is associated with the combustion engine and its aftertreatment system. To achieve maximum performance in both fuel economy and hazardous emissions, the concept of Integrated Powertrain Control (IPC) is presented. This paper presents an overview of TNOs IPC roadmap. It is shown how system integration takes a dominant role in the near future and how complexity of the powertrain is handled in a structured way. The potential benefit of IPC is emonstrated for a commercial hybrid delivery truck case study. Based on simulations with a validated powertrain model, analysis is carried out considering the trade-off between energy management and emission management. It is demonstrated that the CO2 emissions and related operational costs allow for a reduction of 3.5%, whereas NOx emissions can be reduced up to 27%. The added value of IPC is that it targets the NOx emissions exactly at the boundary set by emission legislation whereas the remaining freedom is used to achieve lowest operational costs

Online adaptive approach for a game-theoretic strategy for complete vehicle energy management

This paper introduces an adaptive approach for a game-theoretic strategy on Complete Vehicle Energy Management. The proposed method enhances the game-theoretic approach such that the strategy is able to adapt to real driving behavior. The classical game-theoretic approach relies on one probability distribution function whereas the proposed approach is made adaptive by using dedicated probability distribution functions for different drive patterns. Owing to the adaptability of the proposed approach, the strategy is further refined by proposing dedicated objective functions for the driver player and for the auxiliary player. Next, an algorithm is developed to classify the measured driving history into one of the pre-defined drive pattern and employ the corresponding game-theoretic strategy. Multiple strategies are simulated with a model of a parallel hybrid heavy-duty truck with a battery and electric auxiliaries. The fuel reduction results are compared and the adaptive game-theoretic approach shows an improved and a more robust performance over different drive-cycles compared to the non-adaptive one

Integrated Powertrain Control for optimizing CO2-NOx emission trad-eoff in heavy-duty hybrid electric vehicles

Energy management in modern vehicles typically relates to optimizing the powerflow in the (hybrid) powertrain, whereas emission management is associated with the combustion engine and its aftertreatment system. To achieve maximum performance in both fuel economy and hazardous emissions, the concept of Integrated Powertrain Control (IPC) is presented. This paper presents an overview of TNOs IPC roadmap. It is shown how system integration takes a dominant role in the near future and how complexity of the powertrain is handled in a structured way. The potential benefit of IPC is emonstrated for a commercial hybrid delivery truck case study. Based on simulations with a validated powertrain model, analysis is carried out considering the trade-off between energy management and emission management. It is demonstrated that the CO2 emissions and related operational costs allow for a reduction of 3.5%, whereas NOx emissions can be reduced up to 27%. The added value of IPC is that it targets the NOx emissions exactly at the boundary set by emission legislation whereas the remaining freedom is used to achieve lowest operational costs