Optimization of Hydrogen Refueling Station Operations for Toyota Motor Manufacturing Australia

Thesis Abstract

Hydrogen refueling infrastructure plays a pivotal role in enabling the operational viability of fuel cell electric vehicle fleets within automotive manufacturing environments, yet variability in station reliability, throughput, and energy efficiency poses substantial risks to production schedules and total cost of ownership. This study addresses the problem of suboptimal hydrogen refueling station (HRS) operations at Toyota Motor Manufacturing Australia, where intermittent supply, long refueling times, and compressor energy inefficiencies constrain fleet readiness and overall plant productivity. The aim is to develop an optimization framework that enhances HRS reliability, minimizes downtime, and reduces energy consumption while maintaining safety and compliance with Australian standards. Specific objectives are (i) to quantify current HRS performance using real-time operation logs from a 1.2 MW electrolyzer-based HRS and a 350 bar dispensing system over 24 months (n = 2,400 operational cycles); (ii) to identify bottlenecks in supply chain, equipment maintenance, and control logic through time-series analysis and fault-tree decomposition; (iii) to formulate a mixed-integer linear programming (MILP) model for joint optimization of hydrogen production scheduling, storage management, and dispensing operations under demand uncertainty; (iv) to evaluate the model using scenario analysis and stochastic optimization to derive robust operating policies; and (v) to validate the recommendations via a pilot implementation and performance monitoring over 6 months. The methodological approach integrates a mixed-methods design quantitative data from plant SCADA systems, maintenance logs, and energy meters; qualitative insights from semi-structured interviews with operations engineers (n ? 12) and maintenance technicians (n ? 8); and a risk-adjusted simulation framework. Data collection instruments include archived SCADA time series (hourly resolution), equipment failure records, and a structured survey instrument assessing perceived reliability and safety culture. Validity and reliability are ensured through triangulation, Cronbach’s alpha for survey scales (? ? 0.78), and cross-validation of the MILP model against historical data. The analysis plan comprises time-series decomposition to identify seasonality and anomalies, regression analysis to quantify drivers of downtime, survival analysis for component failure patterns, and an MILP optimization with stochastic programming to handle demand and supply variability. The theoretical foundation draws on the Resource-Based View to link plant assets to competitive advantage, and the Theory of Constraints to prioritize bottleneck alleviation, complemented by the Energy-Optimum Control theory for dynamic management of production, storage, and dispensing. Expected findings include (i) quantified performance gaps in HRS uptime, refueling throughput, energy intensity per kilogram of hydrogen dispensed, and total cost per kilogram; (ii) a validated MILP model that prescribes production scheduling, storage buffering, and demand-responsive dispensing policies under 95th percentile demand scenarios; (iii) recommended control strategies such as predictive maintenance triggers, condition-based lubrication schedules, and adaptive dispatch rules that reduce average refueling time by 12–18% and energy use by 6–10%; and (iv) a risk-adjusted operational playbook tailored to the Australian manufacturing context. The study contributes to knowledge by bridging empirical HRS performance data with a scalable optimization framework, extending the application of MILP-based hydrogen logistics to automotive manufacturing, and integrating safety, reliability, and energy efficiency considerations within a single decision-support tool. The main conclusion is that concurrent optimization of production, storage, and dispensing—informed by real-time data, robust statistical analyses, and scenario-based planning—can substantially improve HRS reliability and economics without compromising safety. Practical recommendations include implementing a modular control architecture with real-time health monitoring, scheduling preventive maintenance during low-demand windows, expanding on-site storage buffers within risk limits, and adopting a rolling horizon optimization with quarterly recalibration to adapt to fleet growth and technology evolution. Future research directions suggest extending the framework to multi-site hydrogen supply networks and incorporating life-cycle cost analysis to capture long-term environmental and financial impacts.