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Cell voltage inconsistency in a battery pack is an important signal released by the deterioration of battery performance. In this paper, voltage inconsistency is categorized into static inconsistency and dynamic inconsistency, and the latter contains progressive fluctuation fault and sudden fluctuation fault. For voltage dynamic inconsistency, this paper innovatively proposes a fault diagnosis and type identification method based on weighted Euclidean distance evaluation and statistical analysis. Specifically, firstly, the Euclidean distance evaluation method is used to quantify the cumulative length of the voltage curve of each individual cell in the time window, and at the same time, different forgetting factors are weighted on the voltage line segments to mitigate the fault diagnostic delay and memory effect defects of the time window. Then, a voltage abnormality evaluation coefficient is introduced to characterize the degree of inconsistent fluctuation of the cell voltage, and statistical methods are used to find a reasonable threshold. Further, fault type identification algorithm layer is conducted to identify the type of cell voltage fluctuation by using the optimized correlation coefficient method while the cell is detected by the fault diagnosis algorithm layer. Finally, the effectiveness of the proposed fault diagnosis strategy is verified by experimental data, and an online platform is utilized to obtain voltage data with different fault characteristics to test the practical application of the method. By comparing the proposed method with various data-driven methods with the same data evaluation significance, the results show that the method in this paper is more robust to static voltage inconsistency and the length of the time window, and is capable to recognize the various data patterns of potential threats, with higher accuracy and computational efficiency. • Euclidean distance evaluation is used for cell voltage inconsistency fault diagnosis. • Forgetting factor is used to mitigate fault diagnosis delay and memory effect defects of time window. • Optimized correlation coefficient is used to isolate pattern of cell voltage inconsistent fluctuations. • The accuracy is more than 99%. [ABSTRACT FROM AUTHOR]

A fault diagnosis method for electric vehicle power batteries based on a time-frequency diagram is proposed. First, the original voltage signal is decomposed by improved variational mode decomposition to eliminate the influence of battery inconsistency on battery feature extraction. Then, the continuous wavelet transform is used to transform the one-dimensional signal into a two-dimensional time-frequency diagram, and the image entropy is used to reflect the characteristic parameters of the battery fault. Finally, the abnormal battery is marked with clustering algorithm. It is verified by real vehicle data that the proposed method can identify the battery fault and advance the identification time. • Decomposing voltage signals to reduce the impact of battery inconsistencies. • Extract battery fault features from time-frequency domains to improve sensitivity. • The clustering algorithm is used to realize the recognition of the fault battery. [ABSTRACT FROM AUTHOR]

The mileage range of electric vehicles is still restricted incredibly due to the limitation of the onboard battery energy and long charging time; therefore, a comprehensive energy consumption optimization of the thermal management system is necessary to guarantee the normal use of electric vehicles. A test bench of integrated battery thermal management system for pure electrical transportation vehicles is established in this study. The optimal thermal management strategy is developed based on analyzing the energy-saving potential of thermal management components. When the battery system operates under real-vehicle operating conditions at representative ambient temperatures, the thermal management system characteristics applied to the proposed strategy are validated and analyzed. The experimental tests show that the proposed thermal management strategy can effectively reduce the energy consumption of the thermal management system under the premise that the battery temperature is controlled within the appropriate range, compared with the prototype vehicle. • The performances of thermal management system based on a real vehicle prototype test. • Analyzation on the energy-saving potential of the energy consuming components for the battery thermal management system. • Experimental investigation of a collaborative control strategy at the real vehicle application level. • Recommendations for optimizing the energy consumption of battery thermal management systems. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the mechanisms of battery is essential for the design of thermal management systems. In this work, a second-order equivalent circuit model considering the hysteresis effect of the battery was developed, and a combined EIS and HPPC characterization method was used for parameter identification. The results showed that the combining EIS and HPPC parameter measurement methods could extract and analyze the parameters of the battery with different time scales and improve the accuracy of the model. It was found that the polarization internal resistance (R 1 and R 2) of the battery shows a stronger temperature dependence than the ohmic internal resistance at low temperatures, with R 1 being about 170 times higher at low temperatures (10 °C) than high temperatures (50 °C). At the same temperature, τ 2 was approximately 100 times larger than τ 1 , which was two orders of magnitude difference between τ 1 and τ 2. In addition, the dynamic heat generation results showed that the heat generation rate of the battery increases with decreasing temperature. At 3C discharge rate, the heat generation rate of the battery at low temperature (−10 °C) was about 1.5 times higher than at room temperature (25 °C). • The time-scale information in the battery is extracted and analyzed. • Relationships between internal parameters and kinetic processes were established. • The dynamic heat generation process of the battery was studied. [ABSTRACT FROM AUTHOR]

The data-driven method is used widely to estimate the state of health (SOH) of the battery, but the selection of data features and the data training methods affect the estimation results greatly. With the stacking algorithm, this paper proposes a multi-feature fusion model to estimate battery SOH by fusing different feature parameters and combining support vector regression (SVR) and long short-term memory network (LSTM). The feature parameters were extracted only from the current change curve of the constant voltage charging stage. The support vector regression based on grid search (GS-SVR) was selected as the primary-learner, and the primary SVR models were constructed through 5-fold cross-validation for different feature parameters. The LSTM was selected as the secondary-learner. With the stacking algorithm, LSTM was used to fuse multiple primary SVR models to form an ensemble learner model to improve the performance of multi-feature fusion. The battery aging test data set and NASA battery test data set were used to evaluate the effectiveness. The results verified the validity and superiority of the proposed method. Compared with the existing estimation methods, root mean square error is reduced by at least 0.11, and mean absolute percentage error is reduced by at least 0.12%. • The input features are extracted from the current change curve during the charging process. • The Stacking algorithm is used to estimate the battery SOH. • LSTM are used to fuse multiple primary SVR models to form an ensemble learner model. • The battery aging test data set and NASA battery test data set are used to evaluate the effectiveness. [ABSTRACT FROM AUTHOR]

Recycling waste batteries for remanufacturing or echelon utilization is conducive to energy storage and the electric vehicle market. To address the distinct difficulties in the process of waste battery-to-reutilization, we build an evolutionary game to model three parties that include the government, manufacturing, and consumers. We find that the choice of strategies for government support, echelon utilization, and consumer participation is an equilibrium. Moreover, we simulate waste batteries-to-reutilization under the subsidy reduction scenario. Our findings demonstrate that: (1) the profits of manufacturers and consumers increase significantly in the first year of subsidies granted by the government; (2) a reduction in subsidies initiates various profit trends across the strategies of echelon utilization and remanufacturing. Generally, the profits earned through echelon utilization are higher than those through remanufacturing during the period of subsidy reduction; and (3) after the subsidies fall to zero, the earnings of manufacturers and consumers increase in the long run. Our paper proposes generalizing the typical mechanism of recycling subsidies for cities and formulating a reasonable path for reducing subsidies. Manufacturers should pay more attention to echelon utilization for the two decision-making schemes of echelon utilization and remanufacturing. • Evolutionary game model of waste battery-to-reutilization is constructed. • Simulate waste battery-to-reutilization under the scenario of subsidy reduction. • Profits of manufacturers and consumers will rebound after the subsidy ends. • Echelon utilization rather than remanufacturing should be aroused more attention. [ABSTRACT FROM AUTHOR]