top of page
Writer's pictureBaba Mulani

Evolution of Battery Management Systems: Challenges in Battery State Estimation


Image Reference: Zhou L, Lai X, Li B, Yao Y, Yuan M, Weng J, Zheng Y. State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, & Future Trends. Batteries. 2023; 9(2):131. https://doi,org/10.3390/batteries9020131

Estimating the battery state is a significant challenge in the 'Battery Management System (BMS)' due to the nonlinear characteristics of Li-ion batteries. The complicated process involves estimating the complex internal state of the battery with limited external signals & predicting the long-term state with short-term test signals.


Evolution of Battery Management Systems (BMS)-


1. Zero Management (1st Gen): Basic tasks such as voltage detection & controlling charge & discharge.


2. Simple Management (2nd Gen): Monitors battery data, including current, voltage, & temperature, from a few batteries. A simple control algorithm to prevent overcharge & over-discharge, enhancing battery safety & lifespan.


3. Advanced Management (3rd Gen): As the number of batteries increased, BMS became more intelligent & sophisticated. Started managing a larger number of batteries, with features like state estimation, fault diagnosis, thermal management, & fast charging. Faces challenges in extreme conditions due to the increasing energy density of batteries.


4. Intelligent Management (4th Gen): Next phase of BMS evolution focuses on long-term, accurate management of large-scale batteries under complex conditions. It adds advanced functions such as ultrafast charging, active safety control, & high-level interactions like vehicle-to-grid (V2G), vehicle-to-home/buildings (V2H/B), & vehicle-to-vehicle (V2V). Technologies like intelligent sensing, big data, AI algorithms, digital twins, & blockchain are integrated into this generation of BMS, enabling active, cooperative management & full life cycle control.


Key Challenges in Battery State Estimation & Proposed Solutions-


1. Online Extraction of Electrochemical Characteristics: Proposed solutions include,

- Developing high-precision, low-cost methods that can measure the internal state of batteries.

- Using advanced models to extract more information from limited external signals like current, voltage, & temperature.


2. Online Identification of Battery Aging Patterns: Possible solutions include,

- Developing methods that can go deeper into the battery to capture the essence of aging, thereby improving the accuracy of State of Health (SOH) estimation.

- Enhancing aging models to better account for long-term & hidden characteristics.


3. Accurate Prediction of Battery Life-cycle Aging Trajectory: Possible solutions include,

- Develop techniques that can accurately map & predict the entire life-cycle aging trajectory.

- Build models robust enough to handle the nonlinearity of battery aging & accurately predict hidden characteristics like the knee point effect.

0 views

Recent Posts

See All
© Copyright
bottom of page