Revisiting the State-of-Charge Estimation for Lithium-Ion Batteries: A Methodical Investigation of the Extended Kalman Filter Approach

With high energy/power density, flexible and lightweight design, low self-discharge rates and long cycle life, lithium-ion (Li ⁺ ) batteries have experienced a surging growth since being commercialized in the early 1990s [1]. They are dominant today in the consumer electronics sector. Due to continually declining manufacturing costs, they are also rapidly penetrating sectors such as the power grid, renewable energy, automotive, and aerospace, where largescale energy storage is needed. Looking into the future, the role of Li ⁺ batteries will be further strengthened as a key energy-storage technology to support the progression of the world into the green energy era. However, their vulnerability to overcharge, overdischarge, and overheating can easily expose them to performance degradation, shortened cycle life, and even fire and explosion, thus raising many concerns about their deployment. These challenges have been driving a massive solution-seeking effort in various relevant research fields. Associated with this trend is the control-theory-enabled advancement of battery-management system (BMS) technologies.