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During driving conditions, when it is needed to transition from Electric Vehicle (EV) to Hybrid Vehicle operation, synchronization of the engine with the shaft and transmission is essential to enable clutch engagement and, subsequently, providing engine power to the wheels. Challenges arise when the engine must generate power to move itself and cannot rely on electric motors for precision. Cost-effective hybrid vehicle propulsion architectures which utilize small 12V belt-starter generators (BSGs) to initiate engine activation are inherently affected. In these situations, a speed profile that balance rapid response and control effort while considering system limitations to mitigate undesirable overshoots and delays, is required. This paper presents a Linear Quadratic Integral (LQI) approach to formulate a speed reference profile that ensures optimal engine behavior.

Engine stall, a noteworthy occurrence in traditional vehicles, poses challenges due to the inability to disconnect the engine from the driveline. Consequently, in such scenarios, the vehicle experiences a loss of propulsion, necessitating the driver to pull over. The severity of propulsion loss events is underscored by regulatory bodies like the National Highway Traffic Safety Administration (NHTSA), potentially leading to costly recalls for Automotive Manufacturers. Therefore, proactive measures to avert Loss of Propulsion (LoP) events, including the exploration of remedial actions, are strongly encouraged during powertrain controls design. In contrast, hybrid electric vehicles offer a unique advantage. Given the ability to connect or disconnect the engine from the driveline in hybrid or electric-only modes, an engine stall in hybrid mode need not result in a complete loss of propulsion.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.