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As a result of global actions for decarbonization, the rapid development of electrified vehicles (EV), including plug-in hybrid vehicles (PHEV) leads to increasing demands for maximizing battery useful life since recycling EV batteries would bring new environmental issues. PHEVs can have a higher fuel economy while maintaining the battery state-of-charge (SoC) through the equivalent consumption minimization strategy (ECMS); however, it is still a great challenge to maximize battery life through PHEV control since it is hard to balance diverse time scale control objectives (fuel economy, SoC control and the battery life). To this end, this paper proposes a Cuboid Equivalent Consumption Minimization Strategy (C-ECMS) for multimode PHEVs. A new concept of the "Hamiltonian matrix" is introduced by adding an additional control degree-of-freedom to the conventional Hamiltonian vector to enable optimal dual motor control in the multi-mode PHEVs. Then, an aging factor (AF) is introduced in associated with the equivalent factor (EF) to generate three Hamiltonian matrices that establish a cuboid knowledge base for the optimal control considering diverse time scale objectives. Experiments under five different driving cycles are conducted to study 1) the impact of Hamiltonian matrix dimensions on SoC control accuracy and 2) the impact of EF and AF settings on the Pareto Frontier considering fuel economy, SoC accuracy, and battery aging. The unified setting for C-ECMS is obtained based on the experimental study, and the result is demonstrated by a comparison study with the rule-based strategy and the standard ECMS implemented in the same PHEV. The results show that the proposed C-ECMS outperforms the standard ECMS control and achieves more accurate SoC sustaining (0.4% of SoC error), less fuel consumption (8.4% improvement), and less battery capacity loss (10.4% improvement). • A cuboid equivalent consumption minimization strategy is proposed for PHEV • Diverse time scale control objectives are incorporated into a new Hamilton matrix • Pareto analysis is conducted to extract the optimal control settings • 8.4% fuel and 10.4% battery capacity loss can be saved by the proposed method [ABSTRACT FROM AUTHOR]

This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline. [ABSTRACT FROM AUTHOR]

• Integrating powertrain optimization and optimal control of a fuel cell mining truck. • Jointly considering energy efficiency and degradation cost of fuel cell and battery. • Multi-level fuel cell powertrain design/control optimization and implementation. • Robust and efficient global optimization method for complex optimization problem. • New methods to apply fuel cell to heavy duty vehicles at reduced lifecycle cost. Proton Exchange Membrane Fuel Cell (PEMFC) system, as a highly efficient, zero pump-to-wheel emission and quick refuelling energy conversion device, is particularly suited for heavy-duty transportation applications. An optimally designed hybrid powertrain with a battery energy storage system and optimal operation control can extend the operating life of the PEMFC system and reduce its lifecycle cost. This work focuses on the minimization of the lifecycle cost of a PEMFC hybrid electric propulsion system through the joint powertrain system design and control optimizations. The approach models and predicts the cost of hydrogen fuel determined by the energy efficiency of the powertrain, as well as the costs associated with the performance degradation and life-shortening of the PEMFC and the battery under a given vehicle operation profile. A nested, tri-level optimization problem is formulated for identifying the optimal powertrain component sizes and energy management parameters, and an advanced surrogate-based global optimization method is used to solve the complex optimization problem. The advantage of the new approach is revealed through the optimal design and control development of a representative mining truck operating under a real-world operation profile, reducing its mining tonnage specific cost by ten percent over its lifecycle. The new integrated design and control optimization method improved energy efficiency, reduced environmental pollutants, and lowered the lifecycle cost of fuel cell mining trucks, contributing to broader applications of the PEMFC technology. [ABSTRACT FROM AUTHOR]