The three dimensional thermal model of a forced air-cooling battery thermal management system (BTMS) using aluminium foam heat sink (AFHS) is established, and the effects of porosity, pore density, and mass flow rate on the thermal and flow performance are discussed numerically from the aspects of pressure drop and temperature control effectiveness. The results reveal that an AFHS can markedly reduce the battery temperature compared with the BTMS without AFHS, but it also causes huge pressure loss and increases the temperature difference between the upstream and downstream of the battery. Reducing the porosity of aluminium foam reduces the battery’s average temperature, but increases the temperature difference. The increase of pore density leads to the increase of pressure drop, but has little effect on the battery temperature. Based on this, a study of the gradient porosity of the AFHS is carried out, and the thermal and flow performance are compared with the homogeneous AFHS. The results show that the AFHS with porosity-increasing gradient pattern (PIGP) in the direction perpendicular to flow reduces the pressure loss and improves flow performance. The AFHS with a porosity-decreasing gradient pattern (PDGP) in the flow direction has no obvious effect on the flow characteristics, but it can reduce the temperature difference of the battery. The direction of gradient porosity can be selected according to need. In addition, due to the energy absorption characteristics of aluminium foam, AFHS can improve the crashworthiness of the battery pack. Therefore, AFHS has great potential in air-cooled BTM.

In order to solve the noise reduction problem of acoustic emission signals with cracks, a method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and wavelet packet (WPT) is proposed and named CEEMD-WPT. Firstly, the single Empirical Mode Decomposition (EMD) used in the traditional CEEMD is improved into the WPT-EMD with a more stable noise reduction effect. Secondly, after decomposition, the threshold value of the correlation coefficient is determined for the Intrinsic Mode Function (IMF), and the low correlation component is further processed by WPT. In addition, in order to solve the problem that it is difficult to quantify the real signal noise reduction effect, a new quantization index “principal interval coefficient (PIC)” is designed in this paper, and its reliability is verified through simulation experiments. Finally, noise reduction experiments are carried out on the real crack acoustic emission dataset consisting of tensile, shear, and mixed signals. The results show that CEEMD-WPT has the highest number of signals with a principal interval coefficient of 0–0.2, which has a better noise reduction effect compared with traditional CEEMD and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Moreover, the statistical variance of CEEMD-WPT is evidently one order of magnitude smaller than that of CEEMD, so it has stronger stability.

The rapid proliferation of electric vehicle adoption has brought about significant changes in energy consumption patterns, but improper disposal of retired batteries poses new challenges to the environment. In order to promote the sustainable development of the industry using retired batteries, this paper focuses on the research on retired battery appearance detection, sorting, and regrouping technologies. Firstly, the standards for retired battery appearance detection are analyzed, and a method of acquiring battery appearance features through two-stage image acquisition is proposed. Machine vision is employed to achieve the appearance filtering of retired batteries, overcoming the shortcomings of manual screening. For the filtered batteries, capacity, internal resistance, and open-circuit voltage are determined as indicators. Analytic hierarchy process and Gray relation analysis are employed for classification based on four application scenarios. Additionally, an improved Gaussian mixture model clustering algorithm is proposed. In the recombination process, the algorithm parameters are adaptively adjusted for each battery category. Experimental results demonstrate that the accuracy of battery appearance filtering exceeds 97%, and the improved algorithm effectively enhances the consistency among batteries. Compared to the baseline algorithm, the performance consistency of regrouping batteries is increased by more than 5%.

Batteries are widely used in our lives, but the inevitable inconsistencies in series-connected battery packs will seriously impact their energy utilization, cycle life and even jeopardize their safety in use. This paper proposes a balancing topology structure combining Buck-Boost circuit and switch array to reduce this inconsistency. This structure can realize multi-cell-to-multi-cell (MC2MC) battery balancing by controlling the switch array and having a fast balancing speed, easy expansion and few magnetic components. Then, the operation principle of the proposed balancing topology is analyzed, and the simulation model is verified. In addition, the effects of switching frequency and voltage difference on the equalization effect are further analyzed. The results show that the higher the switching frequency, the lower the time efficiency, but the higher the energy efficiency. The voltage difference significantly impacts the duty cycle, so it is absolutely necessary to introduce a variable duty cycle in the multi-cell-to-multi-cell equalization. Finally, eight series batteries are selected for simulation verification. The simulation results show that, compared with any-cell-to-any-cell (AC2AC) equalization, the time efficiency of multi-cell-to-multi-cell equalization is improved considerably, the energy efficiency is improved slightly, and the variance of the completed equalization is reduced, demonstrating the excellent performance of multi-cell-to-multi-cell equalization.

Driving style has a significant effect on the operating economy of fuel cell buses (FCBs). To reduce hydrogen consumption and prolong the fuel cell life of FCBs, this paper proposes an online adaptive equivalent consumption minimum strategy (A-ECMS) based on driving style recognition. Firstly, driving data from various drivers is collected, and a standard driving cycle is created. Neural networks are then used to identify driving conditions, and three fuzzy logic recognizers are developed to identify driving styles for different driving conditions. The driving style factor is associated with the equivalent factor using an optimization algorithm that incorporates hydrogen consumption cost and fuel cell degradation cost into the objective function. Simulation results demonstrate that the proposed A-ECMS can reduce equivalent hydrogen consumption, prolong fuel cell life, and result in a 6.2% reduction in total operating cost compared to the traditional method.