Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H 2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

No abstract

Fuel cells (FC) are a clean energy source that are capable of powering a vehicle’s electrical energy requirements whilst providing zero operating emissions. In this study, a full-scaled computer model FC/supercapacitor (SC) hybrid has been developed to investigate the performance of the hybrid propulsion system under real-world performance conditions. A control strategy focused on maintaining a constant FC output at a user-defined value has been developed and applied to the FC/SC hybrid model. Driving cycles collected from a practical double-decker bus have been utilised to evaluate the developed model. It has been demonstrated that the proposed control strategy is capable of maintaining a constant and stable FC output while meeting a real world dynamic load. Based on the obtained results, a general strategy to identify the degree of hybridisation between the FC and the SC in a FC hybrid system has been developed and demonstrated.

No abstract

A supercapacitor module was used as the energy storage system in a regenerative braking test rig to explore the opportunities and challenges of implementing supercapacitors for regenerative braking in an electric drivetrain. Supercapacitors are considered due to their excellent power density and cycling characteristics; however, the performance under regenerative braking conditions has not been well explored. Initially the characteristics of the supercapacitor module were tested, it is well known that the capacitance of a supercapacitor is highly dependent on the charge/discharge rate with a drop of up to 9% found here between the rated capacitance and the calculated value at a 100 A charge rate. It was found that the drop in capacitance was significantly reduced when a variable charge rate, representative of a regenerative braking test, was applied. It was also found that although supercapacitors have high power absorbing characteristics, the state-of-charge significantly impacts on the charging current and the power absorbing capacity of a supercapacitor-based regenerative braking system. This owed primarily to the current carrying capacity of the power electronic converters required to control the charge and discharge of the supercapacitor module and was found to be a fundamental limitation to the utilisation of supercapacitors in a regenerative braking system. In the worst cases this was found to impact upon the ability of the motor to apply the desired braking torque. Over the course of the tests carried out the overall efficiency was found to be up to 68%; however, the main source of loss was the motor. It was found that measurement of the state-of-charge using the rated capacitance significantly over-estimates the efficiency of the system.