In this paper a new approach is developed to optimize the engine and hydraulic element size and their corresponding parameters of a hybrid hydraulic powertrain to given or assumed load/driving cycles. A multi objective optimization algorithm in combination with boundary condition regulation is applied in a loop. In addition, a new mixed optimization algorithm is proposed to overcome the problem of power management optimization by obtaining a better spread of solutions. The main contribution of the paper is the optimal selection of the motor and accumulator size thereby ensuring optimal vehicle dynamic and power consumption properties to different accumulator and engine sizes.978-1-4799-0720-5/13/$31.00 ©2013 IEEE

The field of hybrid vehicles has undergone intensive research and development, primarily due to the increasing concern of depleting resources and increasing pollution. In order to investigate further options to optimize the performance of hybrid vehicles with regards to different criteria, such as fuel economy, battery aging, etc., a detailed state-of-the-art review is presented in this contribution. Different power management and optimization techniques are discussed focusing on rule-based power management and multi-objective optimization techniques. The extent of rule-based power management and optimization in solving battery aging issues is investigated along with an implementation in real-time driving scenarios where no pre-defined drive cycle is followed. The goal of this paper is to illustrate the significance and applications of rule-based power management optimization based on previous contributions.

Abstract:Real-time power management in the presence of one or more reversible energy storage systems is a current issue with hybrid electric vehicles (HEVs). To evaluate the potentials of rule-based power management, optimization with respect to two conflicting objectives, fuel consumption and state of charge (SoC) deviation, is considered in this contribution. A modular structure of power management with decoupled offline and online parts is presented. The online part incorporates look-up tables (LUTs) with parameters from the offline optimization part. This permits an inclusion of more LUTs corresponding to different drive patterns. The goal of this contribution is to combine the real-time applicability of rule-based power management and the multi-objective optimization property of genetic algorithms in a single control strategy. Component aging problems are addressed by suitable design. The influence of sizing is investigated. Finally, an experimental setup consisting of components capable of realizing the dynamics of real powertrain components is realized and introduced. A verification/plausibility assessment of modeled dynamics based on the literature is considered. This newly-introduced concept represents a class of power management, which is easy to implement, can tackle different objectives in real time, and adapt itself to unknown driver demands.

The need for the development of online powermanagement strategies applicable to real-time hybrid powertrain systems is an important issue in the transportation sector. A powermanagement strategy alone does not necessarily ensure optimal power distribution amongst the drive train components. Thus optimization of powemanagement is another task which needs to be considered in terms of multiple objectives. Apart from online applicability of powermanagement optimization, a consideration of useful combinations of drivetrain components such as two storage elements together with a primary source may also be useful. In this contribution, an online powermanagement strategy is applied to a three-source hybrid electric powertrain. An optimization of controller parameters with embedded-online optimization is proposed.

No abstract