As the world pivots towards greener transportation solutions, the safety and efficiency of lithium-ion batteries have emerged as pivotal concerns in the electric vehicle (EV) sector. A recent study led by Xiao-lan Wang from the College of Electrical and Information Engineering at Lanzhou University of Technology addresses these critical issues by enhancing the accuracy of state of charge (SOC) estimations for lithium-ion batteries. Published in the journal ‘工程科学学报’ (Journal of Engineering Science), this research could have far-reaching implications for the commercial viability of electric vehicles.
The impetus for this research stems from the increasing incidents of safety-related accidents involving new energy vehicles, which have raised alarms about the reliability of lithium-ion batteries. “To ensure the safety and longevity of batteries, a robust battery management system is essential,” Wang stated. The SOC is a key parameter that indicates the remaining capacity of a battery, making its accurate estimation crucial for preventing overcharging and overdischarging.
Traditionally, estimating SOC has been fraught with challenges, particularly when using conventional methods like the equivalent circuit model. These methods often struggle with low online estimation accuracy, which can lead to significant safety risks. Wang and her team sought to overcome these limitations by improving the extended Kalman filtering (EKF) algorithm and integrating it with an extreme learning machine (ELM) algorithm. By utilizing the operating voltage and current of the battery as inputs, they developed a model that predicts SOC estimation errors more effectively.
The simulation results from their study reveal a promising enhancement in estimation precision. “Our improved EKF algorithm not only increases accuracy but also reduces the errors introduced by voltage and current measurements,” Wang explained. The fusion model they established effectively bridges the gap between estimation accuracy and the complexity of existing methods, achieving an impressive estimation error of less than 5%.
This advancement holds significant commercial implications for the energy sector. As electric vehicles become more prevalent, ensuring the safety and reliability of their battery systems will be paramount in gaining consumer trust and facilitating widespread adoption. Improved SOC estimation could lead to longer battery life, reduced maintenance costs, and enhanced overall performance of electric vehicles, making them more attractive to both manufacturers and consumers alike.
The research by Wang and her colleagues is a timely contribution to the ongoing evolution of battery technology, reinforcing the importance of innovation in achieving a sustainable future. As the energy sector continues to embrace low-carbon solutions, studies like this one pave the way for safer, more efficient electric vehicles that could transform the transportation landscape.
For more information on this research, visit Lanzhou University of Technology.