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Electrically driven buses have become one of the main vehicle platforms for demonstrating the advantages and capabilities of electric vehicles. This type of vehicle can be powered from several different sources that each have several unique operating characteristics and performance requirements that necessitate novel solutions. In this paper, a novel optimal control strategy based on the next generation range-extended electric bus has been developed. Control strategies play an essential role in extracting the full potential of electric buses and increase their effectiveness at displacing conventional internal-combustion powered buses and thus, reducing global fuel consumption and emissions. Initially, a control-oriented powertrain model was developed in Matlab/Simulink. The series-hybrid powertrain model configuration was then used as the basis to study various control strategies. A new control strategy was devised based on an equivalent consumption minimization strategy (ECMS) to govern the powertrain when operating in different modes. Due to the high impact the drive cycle has on the equivalent factor (EF) in an ECMS, an offline optimization process is performed to further increase the effect of the ECMS on various driving routes. Particle swarm optimization (PSO) was applied to the offline control model, generating the most appropriate EF values for different driving routes. The optimization of the EF values, the implementation of the new control strategy and the control execution process are described here in detail. Finally, the proposed control strategy is validated in the simulation environment by analysing its performance with those of a simple rule-based strategy and a dynamic programming based global optimisation strategy. Comparison of results suggests the superior performance of the proposed method in fuel economy optimization versus the rule-based control method and the similarities to the dynamic programming based control method.

Simulation is a well-established and effective approach to the development of fuel-efficient and low-emissions vehicles in both on-highway and off-highway applications. The simulation of on-highway automotive vehicles is widely reported in literature, whereas research relating to non-automotive and off-highway vehicles is relatively sparse. This review paper focuses on the challenges of simulating such vehicles and discusses the differences in the approach to drive cycle testing and experimental validation of vehicle simulations. In particular, an inner-city diesel-electric hybrid bus and an ICE (Internal Combustion Engine) powered forklift truck will be used as case studies. Computer prediction of fuel consumption and emissions of automotive vehicles on standardised drive cycles is well-established and commercial software packages such as AVL CRUISE have been specifically developed for this purpose. The vehicles considered in this review paper present new challenges from both the simulation and drive-cycle testing perspectives. For example, in the case of the forklift truck, the drive cycles involve reversing elements, variable mass, lifting operations, and do not specify a precise velocity-time profile. In particular, the difficulties associated with the prediction of productivity, i.e. the maximum rate of completing a series of defined operations, are discussed. In the case of the hybrid bus, the standardised drive cycles are unrepresentative of real-life use and alternative approaches are required in the development of efficient and low-emission vehicles. Two simulation approaches are reviewed: the adaptation of a standard automotive vehicle simulation package, and the development of bespoke models using packages such as MATLAB/Simulink.

In this paper a dynamic, modular, 1-D vehicle model architecture is presented that seeks to improve modelling flexibility and can be rapidly adapted to new vehicle concepts, including hybrid vehicles. Interdependencies between model sub-systems are minimized to improve model flexibility. Each subsystem of the vehicle model follows a standardized signal architecture allowing subsystems to be developed, tested and validated separately from the main model and easily re-integrated. Standard dynamic equations are used to calculate the rotational speed of the desired driveline component within each subsystem i.e. dynamic calculations are carried out with respect to the component of interest. Sample simulations are presented for isolated and integrated components to demonstrate flexibility. Two vehicle test cases are presented. The application to a conventional heavy-duty vehicle demonstrates the operational capabilities of the modelling methodology, while the inclusion of electrical components to form a mild-hybrid heavy-duty vehicle shows the model’s potential for predicting improvements in fuel economy and performance over a specified drive cycle. Qualitative validation of the approach is presented, highlighting the ability of the model to accurately capture dynamic events and fuel consumption profiles.