Development of a comprehensive and flexible forward dynamic powertrain simulation tool for various hybrid electric vehicle architectures

Abstract

This paper presents a comprehensive and flexible forward dynamic powertrain simulation tool, WARwick Powertrain Simulation Tool for ARchitectures 2 (WARPSTAR2), for modelling of conventional internal combustion engine, hybrid, and pure electric vehicles. WARPSTAR2 includes physical powertrain component models and their controllers, a hybrid supervisory controller, the driver, and the environment model. The physical powertrain component models are developed in Dymola, while the component controllers, the hybrid supervisory controllers, and the driver model are realized in MATLAB/Simulink. Thus the power of these two software tools is combined. A generalized fuzzy-logic-based supervisory controller is proposed for all hybrid electric vehicle (HEV) architectures so that all HEVs with different architectures share the same structure of supervisory controller. The generalized formation can be used for the supervisory controllers of different HEV architectures with varied parameter settings, thus facilitating the controller design process. The rule-based supervisory controller is also developed in WARPSTAR2. Simulation is carried out for different HEVs with these two supervisory controllers in the driving cycles. The results of engine and battery power usages with these two supervisory controllers are similar and the differences of predicted engine fuel consumptions between the two supervisory controllers are within 5 per cent.

Modelling and Co-simulation of hybrid vehicles: A thermal management perspective

“…This reference can be a legislative drivecycle [41,42], real world logged data [43,44] or custom traces to simulate particular conditions such as vehicle acceleration or gradeability tests [45,46]. Vehicle powertrain models are generally separated into three categories; backward facing [47,48], forward facing [49][50][51] and acausal models [52][53][54].…”

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

“…There are several libraries for control applications, such as jFuzzy-Logic [68], which are noteworthy for making the design of fuzzy logic controllers easier. Many toolboxes can be found for different control problems, among which are the following: the dynamic powertrain simulation tool WARPSTAR2 for the modelling of electric vehicles [69]; a graphical tool aimed at controlling and monitoring temperature and relative humidity in the context of fine agriculture [70]; ASAFES2, a neurofuzzy function approximator, which combines the Takagi-Sugeno fuzzy reasoning method with stochastic reinforcement learning [71]; etc. Moreover, we can find some Matlab toolboxes for control applications, for instance: CIAPS [72] is an open source package based on artificial neural networks and fuzzy logic simulations for the assessment of electrical power systems; Zeng et al developed an expert system which combines ANFIS with genetic algorithms for designing in situ toughened Si 3 N 4 [73]; etc.…”

Modeling, control, and performance of a novel architecture of hybrid electric powertrain system

“…Bougrine et al applied a chemistry-based method and simulated CO and NO emissions models [18]. Also, several researchers have developed modeling tool for HEV architectures with embedded rule-based controllers [19].…”