Traditional Search Process Research Articles

Multi-timescale inconsistency evaluation and data-driven state of health prediction for circulating water-cooled series battery pack

Inter-cell inconsistency significantly challenges battery pack lifetime, performance, and safety. Current research is deficient in evaluation across multiple time scales and correlation analysis with the state of health (SOH) of batteries. In this paper, we innovatively propose a multi-timescale evaluation method using the correlation coefficient to quantify the inconsistency during long-term aging while employing the coefficient of variation to accurately capture short-term inconsistency fluctuations within a single cycle. We extracted 29 health factors from cyclic data to accurately predict SOH and proposed an improved sparrow search algorithm combined with a support vector regression (ISSA-SVR) model. The model optimizes the sparrow position update strategy by introducing a chicken flock optimization algorithm and incorporating a stochastic function mechanism. This improvement effectively reduces the error and uncertainty in the traditional search process. The experiments are carried out on a 1P36 circulating water-cooled series battery pack, verified by rigorous aging tests. The results show that the correlation coefficient is excellent in assessing long-term inconsistency. In contrast, the coefficient of variation accurately maps the subtle differences in short-term variations and is highly correlated with changes in battery capacity. The ISSA-SVR model demonstrates significantly improved accuracy and robustness in SOH prediction compared to other methods.

A Bayesian support tool for morphological design

Dynamic computer based support tools for the conceptual design phase have provided a long-standing challenge to develop. This is largely due to the ‘fluid’ nature of the conceptual design phase. Design evaluation methods, which form the basis of most computer design support tools, provide poor support for multiple outcomes. This research proposes a stochastic-based support tool that addresses this problem. A Bayesian Belief Network (BBN) is used to represent the causal links between design variables. Included in this research is an efficient method for learning a design domain network from previous design data in the structure of a morphological design chart. This induction algorithm is based on information content. A user interface is proposed to support dynamically searching the conceptual design space, based on a partial design specification. This support tool is empirically compared against a more traditional search process. While no compelling evidence is produced to support the stochastic-based approach, an interesting broader design search behaviour emerges from observations of the use of the stochastic design support tool.