Optimization of Power Train and Control Strategy of a Hybrid Electric Vehicle for Maximum Energy Economy

Osornio-Correa, C.; Villarreal-Calva, R.C.; Estavillo-Galsworthy, J.; Molina-Cristóbal, Arturo; Santillán-Gutiérrez, S.D. · 2013 · OpenAlex-citations

DOI: 10.1016/s1405-7743(13)72226-1

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Summary

This paper addresses the optimization of powertrain components and control strategies for parallel Hybrid Electric Vehicles (HEVs) to maximize fuel economy while maintaining vehicle performance and charge sustainability. The study is motivated by the need to balance the efficiency of the Internal Combustion Engine (ICE) and Electric Motor (EM) across varying driving conditions, specifically addressing how to manage energy flow between these sources effectively. The methodology involves developing a heuristic Control Map based on the Basic Engine Operating Line (BEOL) and transmission kinematics. This map plots power demand against vehicle speed to define specific operating zones: conventional, electric, series hybrid, and parallel hybrid. The powertrain configuration analyzed includes a Planetary Gear Set, a Continuous Variable Transmission (CVT), and a Simple Train, allowing for flexible energy flow and regenerative braking. The control strategy utilizes this map alongside a logic system responsive to the Battery State of Charge (SOC), employing specific thresholds (LIB1, LIB2, LIB3) to determine when to charge the battery or switch power sources. Key transmission parameters, such as the CVT torque ratio and gear ratios, are analyzed to minimize belt stress and maximize torque transformation efficiency. The study employs a Multi-Objective Genetic Algorithm (MOGA) to optimize decision variables, including the minimum power threshold for ICE engagement ($P_{min}$), the Simple Train ratio ($R_{ST}$), and the maximum EM assist speed ($V_E$). The optimization balances the trade-off between using the EM (which incurs efficiency losses due to energy conversion) and forcing the ICE to operate off its BEOL (which increases fuel consumption). The Control Map identifies areas where the ICE can generate excess power to charge the battery without leaving its efficient operating line, ensuring charge sustainability. Constraints are applied to prevent battery damage, limiting charging power to four times the nominal battery power. The results demonstrate that the proposed Control Map and optimization strategy effectively increase fuel economy and maintain charge sustainability. By adjusting $P_{min}$, $R_{ST}$, and $V_E$, the system can expand the operational areas where the ICE runs on its BEOL, thereby minimizing energy losses. The study concludes that a coordinated approach optimizing both hardware parameters and control logic is superior to optimizing either in isolation, providing a robust framework for designing efficient parallel HEVs.

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