In a significant advancement for the transportation sector, researchers have unveiled an innovative approach to enhancing the fuel economy of plug-in hybrid electric buses (PHEBs). Led by Lufeng Wang from the College of Intelligent Automobile Manufacturing at Shaanxi College of Communication Technology in Xi’an, this groundbreaking study proposes a multi-layer rule-based energy management strategy (MRB-EMS) combined with powertrain parameter optimization. The research is detailed in the latest edition of the World Electric Vehicle Journal.
The study addresses the pressing need for improved energy efficiency in public transportation, particularly as cities strive to reduce emissions and combat climate change. Traditional fuel vehicles often struggle with adaptability to varying driving cycles, leading to suboptimal fuel economy. Wang emphasizes, “By optimizing energy management strategies and powertrain parameters, we can significantly enhance the performance and efficiency of PHEBs, paving the way for a more sustainable future in urban transport.”
At the core of this research is the establishment of a double-planetary-gear power split model for PHEBs. This model allows for the simultaneous decoupling of rotation speed and torque, a key capability for energy conservation. The MRB-EMS integrates intelligent algorithms with deterministic rules, effectively managing the power distribution between the electric motor and the internal combustion engine based on real-time operational data.
The results are impressive. The enhanced MRB-II-EMS demonstrated a reduction in fuel consumption by 12.02% and 10.35% under the China City Bus Circle (CCBC) and synthetic driving cycles, respectively. Moreover, it significantly decreased battery life loss by 33.33% and 31.64%. Such advancements not only promise to lower operational costs for transit authorities but also contribute to a greener urban environment.
Wang’s research further introduces a combined multi-layer powertrain optimization method utilizing Genetic Algorithm-Optimal Adaptive Control of Motor Efficiency-Particle Swarm Optimization (GOP). This method allows for simultaneous optimization of powertrain parameters, enhancing the electric driving mode and the Shutdown Charge Hold mode. After optimization, fuel consumption was reduced by 9.04% and 18.11%, with battery life loss decreasing by 3.19% and 7.42%. “This dual approach not only boosts fuel economy but also extends the operational lifespan of critical components, which is vital for the sustainability of public transport systems,” Wang notes.
The implications of this research extend beyond academic interest; they signal a potential shift in how cities manage public transportation energy use. As urban centers increasingly adopt electric and hybrid vehicles, the findings could influence fleet management strategies and inform future vehicle designs, ultimately leading to a more efficient and environmentally friendly public transport system.
For those interested in further exploring this pivotal research, more information can be found at the College of Intelligent Automobile Manufacturing. The study’s findings underscore the importance of innovative energy management in shaping the future of transportation, as cities seek to transition to cleaner, more efficient systems. Published in the World Electric Vehicle Journal, this research sets a promising precedent for the evolution of hybrid technology in urban settings.