Energy Management Strategies for Vehicular Electric Power Systems

Abstract

Abstract-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.Index Terms-Energy management, fuel reduction, hybrid electric vehicles (HEVs), regenerative braking, vehicular electric power system.

Hybrid Control of Container Cranes

“…Some dynamic effects can still be incorporated in the model, such as the startup fuel cost of the engine or the influence of the state of charge of the ultracapacitors. This approach has been successfully used for energy management design in the past, see for instance Koot et al (2005); Kessels (2007); Sciarretta et al (2004). Figure 3 shows the measurement data and the model that was created based on laboratory tests on the diesel engine.…”

Predictive energy management for hybrid electric vehicles - Prediction horizon and battery capacity sensitivity

“…In Koot et al (2005) the authors approximate the cost criterion with a second order polynomial function of the manipulated variable and then use quadratic programming to solve the optimization problem. While this method considerably reduces computational time, it is not suited for the combined hybrid energy management purpose since a second order polynomial function of manipulated variables and wheel set point (T o , w o ) is not representative of CO 2 emission.…”

Plug-in hybrid electric vehicles in dynamical energy markets

“…with J(P s ) =ṁ(P s ) − λP s , see [9], [10]. The first terṁ m(P s ) [g/s] describes the instantaneous fuel consumption of the ICE.…”

“…Depending on the actual driving situation, λ changes and therefore, it needs to be on-line estimated (see e.g. [10] for a solution with a proportional integral controller). Its dimension is explained by the fact that λ represents the conversion efficiency for transforming fuel into electric energy.…”