Researchers have proposed a novel active equalization scheme for lithium-ion batteries that uses path planning to address cell inconsistency in battery packs. The approach combines a flexible reconfigurable equalization topology, graph-based energy-transfer modeling, an equilibrium optimizer algorithm, and adaptive battery grouping to improve equalization speed, accuracy, and robustness.
Cell inconsistency is a persistent challenge in lithium-ion battery packs. Even when cells are manufactured to similar specifications, differences in capacity, internal resistance, temperature exposure, aging state, and operating history can cause cells to drift apart over time. If these differences are not managed, the weakest or most imbalanced cells can limit the usable capacity of the whole pack, reduce efficiency, accelerate degradation, and complicate battery management.
Equalization strategies are therefore an important part of battery management systems. Passive equalization can dissipate excess energy as heat, while active equalization aims to transfer energy more intelligently between cells. However, designing an active equalizer that is fast, accurate, efficient, and flexible across different battery-pack conditions remains difficult. The new study addresses this problem by proposing a flexible reconfigurable topology that can operate in multiple equalization modes.
A key idea in the work is to treat the equalization topology as a directed graph. According to the article, the researchers establish an energy transfer matrix and use it to plan how energy should move through the battery system. This graph-based representation allows the equalization problem to be framed as a route-planning task, rather than only as a fixed circuit-switching problem. In practical terms, that means the system can choose more suitable energy transfer routes under different imbalance conditions.
To optimize those routes, the study applies the equilibrium optimizer, or EO, algorithm. The paper reports that the EO algorithm is used to identify energy transfer paths that can achieve a high degree of equalization while reducing energy loss. This is important because an equalization scheme that only moves energy quickly may still be inefficient if it creates unnecessary transfers or dissipates too much energy during the process.
The researchers also introduce an adaptive battery grouping algorithm based on energy change during equalization. Instead of treating all cells in a static way, the method can group batteries according to changing equalization conditions. Such grouping is intended to improve performance by allowing the equalization strategy to respond to the actual state of the battery pack during operation, which may be especially valuable in packs with many cells or changing thermal conditions.
Experimental comparisons reported in the paper suggest that the proposed equalization scheme performs better than several existing switched-capacitor equalizers. The authors state that it is faster than the chain-structured switched-capacitor cell equalizer, the series-parallel switched-capacitor cell equalizer, and the optimized switched-capacitor cell equalizer in terms of equalization accuracy and speed. These comparisons indicate that combining flexible topology with route optimization can improve both how quickly and how precisely cell imbalance is reduced.
The study also tests the method beyond a single narrow scenario. According to the article, comparisons between different strategies, robustness under variable temperature, and feasibility under multiple-cell conditions were added to evaluate performance. The results show that the proposed scheme maintained superior equalization performance, suggesting that the approach may have value for more complex and realistic battery-pack conditions.
Further validation will still be needed before the scheme can be generalized to all battery management systems, especially across larger packs, different cell chemistries, hardware constraints, and long-term aging conditions. Even so, the study offers a strong indication that graph-based path planning and adaptive grouping can make active equalization more flexible and efficient. For lithium-ion battery packs used in electric vehicles and energy storage systems, better equalization could help improve usable capacity, reliability, and long-term pack performance.
Reference
Author:
Xinghua Liu a , Tianyu Ma a , Jiaqiang Tian b , Tianhong Pan b , Xu Zhang c , Peng Wang d
Title of original paper:
A Novel Active Equalization Scheme for Lithium-ion Batteries Using Path Planning
Article link:
https://www.sciencedirect.com/science/article/pii/S2773153725000921
Journal:
Green Energy and Intelligent Transportation
DOI:
10.1016/j.geits.2025.100342
Affiliations:
a School of Electrical Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710061, China
b School of Electrical Engineering and Automation, Anhui University, Anhui 230039, China
c Institute for Advanced Research, Anhui University of Science and Technology, Anhui 230061, China
d School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
Green Energy and Intelligent Transportation
Experimental study
Not applicable
A Novel Active Equalization Scheme for Lithium-ion Batteries Using Path Planning
14-Feb-2026