Improved Mayfly Algorithm Enhances Range-Extended Electric Truck Efficiency
In a significant advancement for range-extended electric vehicles (R-EEVs), researchers from Kunming University of Science and Technology have developed a novel energy management strategy that significantly improves vehicle performance and efficiency. The study, led by Sun Yunxiang, Wang Guiyong, and Wang Weichao, introduces an innovative approach using an improved mayfly optimization algorithm (IMA) to optimize the energy management system of a lightweight R-EEV truck. This breakthrough not only enhances the vehicle’s overall performance but also sets a new standard for the integration of advanced algorithms in automotive engineering.
The research, published in the journal Mechanical Science and Technology for Aerospace Engineering, addresses the critical challenge of balancing fuel economy, emissions, and battery life in R-EEVs. These vehicles, which combine an electric drive system with an auxiliary power unit (APU) typically powered by a small internal combustion engine, offer a practical solution to the range limitations of pure electric vehicles. However, optimizing the energy management system to ensure efficient and sustainable operation has been a complex task, particularly in dynamic driving conditions.
Sun Yunxiang, a master’s student at Kunming University of Science and Technology, and his team recognized the need for a more sophisticated control strategy that could adapt to varying driving conditions while maintaining optimal performance. Traditional energy management strategies, such as the thermostat and power-following methods, often fall short in achieving a balanced performance across multiple objectives. The thermostat strategy, for instance, focuses on maintaining a constant operating point for the APU, which can lead to suboptimal fuel consumption and increased emissions. On the other hand, the power-following strategy, while more responsive to changes in demand, can result in frequent switching of the APU, leading to reduced efficiency and increased wear on the engine.
To overcome these limitations, the team developed a multi-point energy management control strategy based on the improved mayfly optimization algorithm. The IMA is a bio-inspired optimization technique that mimics the mating behavior of mayflies. It combines elements of particle swarm optimization, genetic algorithms, and firefly algorithms, providing a robust framework for solving complex optimization problems. The key innovation in the IMA is the introduction of dynamic self-learning and global learning factors, which enhance the algorithm’s ability to explore the solution space in the early stages of optimization and converge quickly in the later stages.
The researchers began by establishing a multi-objective optimization model for the R-EEV, taking into account various performance metrics such as fuel economy, emissions (NOx, HC, CO), and battery life. The model was designed to evaluate the overall performance of the vehicle under different operating conditions. To ensure the accuracy and reliability of the optimization process, the team used experimental data to calibrate the performance indicators for each component of the powertrain, including the engine, generator, and battery.
The optimization process involved defining the solution space using the APU’s speed and torque as variables. Each mayfly individual in the algorithm represented a potential solution, and the fitness function was designed to minimize the sum of the weighted performance indicators. The IMA was then applied to perform offline optimization, searching for the optimal operating points that would maximize the vehicle’s overall performance.
One of the key challenges in implementing the optimized energy management strategy was ensuring real-time control of the APU. To address this, the team developed a fuzzy controller that uses the vehicle’s demand power and the state of charge (SOC) of the battery as input parameters. The fuzzy controller dynamically adjusts the minimum hold time of the APU’s current operating point, preventing frequent switching and ensuring stable operation. The controller’s rules were designed to balance the trade-offs between fuel efficiency, emissions, and battery health, based on the real-time driving conditions.
The effectiveness of the proposed multi-point energy management control strategy was evaluated through extensive simulations using MATLAB/Simulink and CRUISE, a widely used software for vehicle dynamics and powertrain simulation. The simulations were conducted under the World Light Vehicle Test Procedure (WLTP), a standardized driving cycle that closely mimics real-world driving conditions. The results were compared with those obtained using the traditional thermostat and power-following strategies.
The simulation results demonstrated a significant improvement in the overall performance of the R-EEV when using the IMA-based multi-point energy management strategy. Compared to the thermostat strategy, the new approach achieved a 16.2% improvement in comprehensive performance, while it outperformed the power-following strategy by 7.8%. The improvements were particularly notable in terms of fuel economy and emissions. The IMA-based strategy reduced the specific fuel consumption by 8.328 g/(kW·h) compared to the power-following strategy, and it also achieved lower emissions of NOx, CO, and THC.
In addition to the performance gains, the IMA-based strategy also contributed to better battery management. The battery’s SOC variation was kept within a moderate range, reducing the risk of overcharging and extending the battery’s lifespan. The absolute ampere-hours (A·h) accumulated over the test cycle were significantly lower than those observed with the thermostat strategy, indicating a more balanced and sustainable use of the battery.
The researchers also conducted a detailed analysis of the APU’s operating conditions under the different control strategies. The results showed that the IMA-based strategy effectively reduced the frequency of APU switching, leading to more stable and efficient operation. This, in turn, contributed to the overall improvement in vehicle performance and reduced wear on the engine.
The study’s findings have important implications for the future of R-EEV technology. By demonstrating the effectiveness of the IMA-based multi-point energy management strategy, the researchers have provided a valuable tool for automotive engineers and manufacturers. The strategy can be adapted to different vehicle platforms and driving conditions, making it a versatile solution for improving the efficiency and sustainability of R-EEVs.
Moreover, the use of advanced optimization algorithms like the IMA highlights the growing importance of computational methods in automotive engineering. As vehicles become increasingly complex, with multiple interacting systems and components, the ability to optimize performance through sophisticated algorithms will become even more critical. The IMA, with its enhanced global search capabilities and faster convergence, represents a significant step forward in this direction.
The research also underscores the importance of interdisciplinary collaboration in addressing complex engineering challenges. The team at Kunming University of Science and Technology brought together expertise in mechanical engineering, computer science, and control systems to develop a comprehensive solution. This collaborative approach is likely to be a hallmark of future advancements in the field.
In conclusion, the work by Sun Yunxiang, Wang Guiyong, and Wang Weichao represents a significant contribution to the field of R-EEV energy management. Their innovative use of the improved mayfly optimization algorithm has not only improved the performance of a lightweight R-EEV truck but also set a new benchmark for the integration of advanced algorithms in automotive engineering. As the automotive industry continues to evolve, the insights gained from this study will undoubtedly play a crucial role in shaping the future of sustainable transportation.
Sun Yunxiang, Wang Guiyong, Wang Weichao, et al., Mechanical Science and Technology for Aerospace Engineering, DOI: 10.13433/j.cnki.1003-8728.20230067