Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches.

The trend towards shorter supply chains and home delivery has rapidly increased delivery van traffic. Consequently, in the 20 years prior to 2018, delivery traffic has increased by 71%, while passenger vehicles have increased only by 13%. This drastic change in traffic patterns presented new challenges to decision makers and fortunately coincided with changes in the automotive industry, i.e., the advent of automation. However, the design of a controller is not straightforward due to the complex and nonlinear vehicle dynamics and the nonlinear relationship between the controller, tracking error and trajectory. This paper proposes a novel hybrid artificial-intelligence-based lateral control system for an autonomous delivery van to address these challenges to achieve the lowest value of tracking error. The strategy consists of multiple simultaneously operating fuzzy controllers. Their output signals are optimally weighted by a genetic algorithm to determine the proper allocation of control signals for calculating the final steering angle. Six different scenarios are implemented to evaluate the algorithm. A comparative analysis is then performed with two alternative state-of-the-art methods: (i) manually weighted and (ii) geometrically weighted controllers. During the tests, the vehicle’s speed varied, and the roads considered ranged from simple roads to a series of curves. The results show that the proposed strategy leads to a reduction of up to 91.2% and 61.1% in tracking error compared to the manually and geometrically weighted alternatives, respectively.

Hybrid heavy-duty vehicles powered by multiple fuel cells (FCs) offer opportunities to improve energy efficiency and support sustainable powertrain operation through powertrain-level optimization. This study investigates optimal gear shifting to reduce the electrical-power demand of the electric machine (EM) and optimal power distribution among multiple FCs with different ageing states. A longitudinal vehicle model determines the wheel power demand and propagates the operating point backward through the drivetrain. An optimization-based gear-selection strategy identifies the gear that minimizes the required EM power, while a closed-form optimization method optimally distributes a given total FC power demand among multiple FCs according to the specific electrical energy output and remaining useful life of each FC. An active-set procedure enforces FC operating limits. The methods are evaluated using a Paris–Munich ESCALATE drive cycle and several FC age configurations. Depending on the operating point, optimal gear selection reduces EM power demand by up to 50 kW, while optimal FC power distribution reduces hydrogen-consumption rate by up to $3.1\%$ and $3.3\%$ compared with equal power distribution for dual and three-FC configurations, respectively. The optimized allocation may also have implications for long-term FC ageing and maintenance planning, providing an additional perspective for future assessments of powertrain sustainability.