In conclusion, it is proved that the SVD-AECKF algorithm can improve the joint state estimation of lithium-ion batteries and increase the estimation efficiency and accuracy. This study provides a theoretical basis for providing battery condition monitoring to ensure that drivers can drive the new energy vehicles safely.
The test platform of this experiment mainly includes a temperature laboratory, charge and discharge test, and BMS. A ternary lithium-ion battery with a standard capacity of 72 Ah is selected for lithium-ion batteries. The test platform and related parameters of the ternary lithium-ion battery are shown in Fig. 9. Fig. 9. The experimental platform.
To test and validate the proposed approach, a low-cost battery management system (BMS) was developed, supporting up to 168 cells in series and n cells in parallel. When compared with the multistage charging algorithm, the proposed charging algorithm revealed a shorter charging time (7.85%) and a smaller temperature increase (32.95%).
One of the most promising solutions is lithium–sulfur batteries (Li–S), which replace the cobalt cathodes of conventional Li-ion batteries with sulfur cathodes, producing thinner, lighter and lower-cost lithium batteries [ 4, 5 ]. However, new lithium battery technologies raise several concerns.
However, new lithium battery technologies raise several concerns. A key concern is the charging approach, essential to improve efficiency, charging time, battery aging and lifespan, and to avoid battery overcharging, one of the most severe safety problems for the large-scale application of Li-ion batteries [ 6 ].
Due to the complex and changeable conditions of the LIBs in the practical application of new energy electric vehicles, to further verify the effectiveness and feasibility of the joint algorithm in the practical application, experiments were carried out under BBDST conditions.
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