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During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety.

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

The actual energy saving effect of plug-in hybrid vehicles (PHEVs) is usually evaluated by the electricity utility factor (UF) in a standardized charging pattern. To further evaluate the impacts of the charging pattern heterogeneity of PHEV, actual vehicle travel data are adopted to classify the charging pattern in seven typical Chinese cities and derive its impacts on actual UF. Additionally, UF curves are fitted as power exponential functions. The result shows that during daily usage, UF can reach over 0.8 for 50 km all-electric-range PHEVs for the 77% frequently charging adopters, while it is as low as 0.1 for the 3% rarely charging adopters. Comprehensive UF values at an actual charging pattern are 0.53 and 0.68 for the 50 km and 80 km all-electric-range PHEVs, and the values are 0.03 and 0.04 smaller than the standard UF, respectively.

Promoting Plug-in Hybrid Electric Vehicles (PHEVs) is a promising strategy to mitigate the climate change and improve the environment in the road transportation sector. To evaluate the complex energy saving and GHG emission reduction contribution of on-board fuel and electricity, utility factor (UF) is the most widely adopted parameter to weight the distance driven by electricity and on-board fuel, respectively. However, few researches focus on city-level actual utility factors based on quantitative analysis of the influencing factors, especially in China. In this research, a probability-distribution-based PHEV UF function is constructed from travel pattern parameters, charging frequency and PHEV charge depleting range. Then the urban city-level UF at actual travel and charging patterns are derived from PHEV driving big data in seven typical cities in China. The sales-weighted actual UF is 0.49 and 0.64 for a 50 km-electric-range PHEV and a 80 km-electric-range PHEV; therefore, PHEVs with over 80 km electric range are recommended in China urban areas due to better energy saving and emission reduction potential. Finally, carbon and pollutant emissions are analyzed, actual PHEV carbon emission is 133–137 g/km currently while it could drop to 71–88 g/km at a grid with 80% renewable energy; therefore, cleaner grid would largely contribute to PHEV environmental effects.