In a significant advancement for the electric vehicle (EV) industry, researchers led by Feng-wen Pan from the State Key Laboratory of Automotive Simulation and Control at Jilin University have developed a robust method for estimating the state of charge (SOC) in lithium-ion batteries. This research, published in the journal ‘工程科学学报’ (Journal of Engineering Science), addresses a critical challenge that has long hindered the accuracy of battery management systems.
As electric vehicles become more prevalent, precise SOC estimation has emerged as a crucial factor influencing battery performance and lifespan. Traditionally, many SOC estimation techniques have relied on fixed battery model parameters, often overlooking the impact of temperature fluctuations. This oversight can lead to significant inaccuracies in SOC readings, ultimately affecting vehicle range and safety.
Pan’s team tackled this issue by introducing a robust H∞ filter-based method that takes temperature variations into account. “Our approach models the influence of temperature on battery parameters as an external disturbance,” Pan explained. This innovative perspective allows for a more accurate representation of battery behavior under varying environmental conditions.
The researchers utilized a second-order Thevenin equivalent circuit model, which incorporates two parallel resistor-capacitor pairs to simulate battery dynamics. By applying the sliding linear method to linearize the model, they successfully designed a robust H∞ filter for SOC estimation. The effectiveness of this new method was validated against four different dynamic current load profiles, including the rigorous Beijing Dynamic Stress Test and the Federal Urban Driving Schedule. The results demonstrated that the proposed approach significantly outperformed traditional Kalman filter methods, achieving higher SOC estimation accuracy even as model parameters fluctuated with temperature.
“The robustness of our method under external disturbances opens new avenues for improving battery management systems,” Pan noted. This advancement could lead to longer-lasting battery life and enhanced performance for electric vehicles, addressing one of the industry’s most pressing challenges.
As the demand for electric vehicles continues to surge, this research could have far-reaching implications for manufacturers and consumers alike. Improved SOC estimation can lead to more reliable battery performance, ultimately enhancing consumer confidence in EV technology. With the global push towards sustainable energy solutions, the commercial impact of such innovations is poised to be substantial, potentially accelerating the transition to electric mobility.
This groundbreaking work not only highlights the importance of adaptive technologies in the energy sector but also underscores the critical role of research in shaping the future of transportation. For more information on this research, you can visit lead_author_affiliation.