A Novel Equivalent Consumption Minimisation Strategy for a Series Hybrid Electric Vehicle

In this paper, a novel Equivalent Consumption Minimization Strategy (ECMS) is developed for a series hybrid powertrain system, based on the flow of energy through energy sources and components. The performance of the controller is assessed using both urban and highway drive cycles, through comparing results for the series hybrid powertrain system against a comparable conventional powertrain. Fuel economy improvements are achieved by effectively optimizing the distribution of the power generation within the power sources of the system and by promoting battery charging during low-power demand conditions. The simulation results show that the proposed ECMS provides a substantial improvement to fuel economy and energy consumption of a series hybrid electric vehicle, whilst effectively managing battery state of charge. Introduction In recent years, there has been a growing trend within the automotive sector for more environmental friendly vehicles due to unsustainable natural resources and growing understanding of the impact of the current transportation fleet on both local and global air quality. Hybrid Electric Vehicles (HEVs) are considered as more sustainable and cleaner alternatives to current Internal Combustion Engine (ICE) vehicles. Typically, a HEV uses two or more energy sources for power generation, providing greater fuel economy and lower vehicle emissions. However, multiple energy sources introduce the need of a sophisticated control system to optimally distribute the power flow within the energy sources to achieve the desired objectives such as fuel consumption minimization and emission reduction. Different control strategies have been designed so as to fulfill the necessary instantaneous power demand of the vehicle, whilst the State of the Charge (SOC) and overall fuel consumption are managed to meet the overall energy demands of the journey. In general, current power management control systems can be classified as rule-based and optimization-based strategies. Rule-based control strategies work by considering set regions of vehicle performance and using predetermined operation modes to function within these regions, such as pure electric operation, pure ICE operation, hybrid drive and kinetic energy recuperation. As rule-based strategies are not able to adapt to changing driving behaviors, fuel economy and vehicle emission improvements do not transfer between different drive cycles, thus limiting the application of rule-based strategies to a specific driving cycle. In contrast, optimization-based strategies are often used to overcome this problem through continuously adjusting power generation based upon current vehicle demands, thereby allowing for robust improvements to fuel economy and vehicle emissions reduction in different driving cycles [1]. Equivalent Consumption Minimization Strategy (ECMS) is known as one of the most successful optimization-based strategies for power management of HEVs. Originally, ECMS was developed for parallel HEVs and its effectiveness on parallelhybrid powertrains has been widely investigated [2-5]. The ECMS was developed based on the idea that discharging the battery at any time is equivalent to some fuel consumption in the future and vice-versa. However, it has been reported in [6] that ECMS might cause significant wheel-torque oscillation due to instantaneously adjusting the power sources being used, thereby affecting system stability. It also might cause the potential for a delayed torque response during initiation of alternative power source, thereby reducing drivability and passenger comfort. These issues can be mitigated or even eliminated in a series or a parallel-series hybrid powertrain, because of decoupling engine power from the road load within these powertrain architectures [6]. Recently, ECMS has been modified and applied to different hybrid powertrain architectures. For instance, Zhang and Vahidi [7] modified the ECMS algorithm by on-line ECMS parameter tuning using partial route preview information. Liu and Peng [8] developed an ECMS with kinematic constraints for a power-split hybrid powertrain while He et. al. [9] proposed an adaptive ECMS for such hybrid powertrains using predictive traffic information. Wang et. al. [10] proposed an adaptive ECMS which can be updated through drive cycle recognition for the power-split hybrid city buses. The application of ECMS on a series hybrid city bus was investigated by [11]. Also ECMS was modified and