Online Power Management with Embedded Offline-Optimized Parameters for a Three-Source Hybrid Powertrain with an Experimental Emulation Application

Abstract

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.

Experimental Implementation of Power-Split Control Strategies in a Versatile Hardware-in-the-Loop Laboratory Test Bench for Hybrid Electric Vehicles Equipped with Electrical Variable Transmission

“…Regarding the MiL vs. HiL results, some inevitable mismatches related to noises were observed. These inevitable deviations could be expected, as has also been widely observed in the results of similar studies performed previously such as in [8,25,26,62]. This can be interpreted as mismeasurements imposed on the sensors by the laboratory's environmental conditions, machine vibrations, or abrupt accelerations of the induction machines.…”

“…In that test bench, the objective was to combine relatively low-cost off-the-shelf products to examine the test environment and realize phases of real-time data acquisition, signal generation, automatic code generation, and power measurements using MATLAB/Simulink ® , dSPACE, and a Hioki power analyzer. The applicability of combining RB and OB EMS control were proposed in [26], while the experimentally-determined parameters of the components were considered instead of real drivetrain components for considering motor/generator dynamics and battery. Li et al [5] performed a HiL analysis for EMS of a parallel HEV by a rapid prototyping approach.…”

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

“…Online strategies involve real-time control and decision making based on current driving conditions and vehicle states. These strategies adaptively adjust the power distribution between the engine and the electric motor to optimize performance and efficiency [6,34,[80][81][82][83]. Online control strategies are based on local optimization and can be implemented in real-time since no driving cycle prior knowledge is required and there is a limited computational load [2,6,10,20].…”

“…Online control strategies are based on local optimization and can be implemented in real-time since no driving cycle prior knowledge is required and there is a limited computational load [2,6,10,20]. Offline energy management strategies are designed before real-time implementation using historical driving data or simulation models and provide optimized power management solutions [53,68,71,82,83]. Offline optimization requires a priori knowledge about upcoming driving cycles and searches for a global optimal solution [2].…”