In the near future, a significant increase in electric power consumption in vehicles is expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution, and consumption of electric power will be used. Inspired by the research on energy management for hybrid electric vehicles (HEVs), this paper presents an extensive study on controlling the vehicular electric power system to reduce the fuel use and emissions, by generating and storing electrical energy only at the most suitable moments. For this purpose, both off-line optimization methods using knowledge of the driving pattern and on-line implementable ones are developed and tested in a simulation environment. Results show a reduction in fuel use of 2%, even without a prediction of the driving cycle being used. Simultaneously, even larger reductions of the emissions are obtained. The strategies can also be applied to a mild HEV with an integrated starter alternator (ISA), without modifications, or to other types of HEVs with slight changes in the formulation.

http://www.mate.tue.nl/mate/pdfs/5064.pdf

Energy management strategies for vehicular electric power systems

Model predictive control power management strategies for HEVs: A review

In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

/pdf/real-time-distributed-economic-model-predictive-control-for-3odos5df0z.pdf

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

A review on idling reduction strategies to improve fuel economy and reduce exhaust emissions of transport vehicles.

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.

/pdf/energy-management-for-automotive-power-nets-10ih6ikree.pdf

Energy management for automotive power nets

Adaptive ECMS: a Causal Set-theoretic Method for Equivalence Factor Estimation∗

Receding horizon control for distributed energy management of a hybrid truck with auxiliaries

Energy management for vehicle power nets

Battery temperature has large impact on battery power capability and battery life time. In Hybrid Electric Heavy-duty trucks (HEVs), the high-voltage battery is normally equipped with an active Battery Thermal Management System (BTMS) guaranteeing a desired battery life time. Since the BTMS can consume a substantial amount of energy, this paper aims at integrating the Energy Management Strategy (EMS) and BTMS to minimize the overall operational cost of the truck (considering diesel fuel cost and battery life time cost). The proposed on-line strategy makes use of the Equivalent Consumption Minimization Strategy (ECMS) along with a physics-based approach to optimize both the power split (between the Internal Combustion Engine (ICE) and the Motor Generator (MG)) and the BTMS’s operation. The strategy also utilizes a quasi-static battery cycle-life model taking into account the effects of battery power and battery temperature on the battery capacity loss. Simulation results present an appropriate strategy for EMS and BTMS integration, and demonstrate the trade-off between the total vehicle fuel consumption and the battery life time.

Jtba John Kessels

Papers

Adaptive ECMS: a Causal Set-theoretic Method for Equivalence Factor Estimation∗

Receding horizon control for distributed energy management of a hybrid truck with auxiliaries

Energy management for vehicle power nets

Cost-Effective Energy Management for Hybrid Electric Heavy-Duty Truck Including Battery Aging

Vehicle Energy Management for On/off Controlled Auxiliaries: Fuel Economy vs. Switching Frequency