With the increasingly serious problem of environmental pollution, new energy vehicles have become a hot spot in today’s research. The lithium-ion battery has become the mainstream power battery of new energy vehicles as it has the advantages of long service life, high-rated voltage, low self-discharge rate, etc. The battery management system is the key part that ensures the efficient and safe operation of the vehicle as well as the long life of the power battery. The accurate estimation of the power battery state directly affects the whole vehicle’s performance. As a result, this paper established a lithium-ion battery charge state estimation model based on BP, PSO-BP and LSTM neural networks, which tried to combine the PSO algorithm with the LSTM algorithm. The particle swarm algorithm was utilized to obtain the optimal parameters of the model in the process of repetitive iteration so as to establish the PSO-LSTM prediction model. The superiority of the LSTM neural network model in SOC estimation was demonstrated by comparing the estimation accuracies of BP, PSO-BP and LSTM neural networks. The comparative analysis under constant flow conditions in the laboratory showed that the PSO-LSTM neural network predicts SOC more accurately than BP, PSO-BP and LSTM neural networks. The comparative analysis under DST and US06 operating conditions showed that the PSO-LSTM neural network has a greater prediction accuracy for SOC than the LSTM neural network.

To improve the discharge equalization efficiency of the battery and prevent the occurrence of overdischarge, in this paper, the 18,650 ternary lithium battery is taken as the object of investigation, and an alternating equalization control system for the discharge process of serial cells is proposed. The system implements the alternating discharge of serial cells by switching on and off, using state of charge (SOC) as the equalization variable, and eventually completes the equalization control of the entire battery pack. Discharge simulations were performed in Matlab/Simulink for faulty and normal operating conditions of the battery pack, respectively. The findings indicate that even in the presence of a malfunction, the battery pack can continue to operate continuously for a while; in contrast, under ideal circumstances, the battery pack is capable of maintaining SOC balance throughout the discharge process. Eventually, five batteries are used to construct the experimental platform for the alternating equalization system. The battery pack can still perform selective discharge under fault conditions until the battery pack reaches the discharge cutoff condition. Under normal conditions, the maximum SOC difference of all five batteries can stabilize at about 1%. The experimental results show that the proposed equalization control system can achieve the equalization of battery discharge and prolong the discharge time, and can prevent the occurrence of battery over discharge.

Introduction: Career indecision is a prevalent developmental challenge among emerging adults, yet the mechanisms through which peer influence relates to decisional difficulty remain poorly understood. Drawing on Social Cognitive Career Theory (SCCT) and the career calling literature, this study tested a moderated mediation model in which positive peer career support and negative peer career pressure are linked to career indecision through career calling, with internalized prosocial values moderating the association between negative peer pressure and calling. Methods: Survey data from 524 undergraduates at a comprehensive Chinese university were analyzed using confirmatory factor analysis, latent moderated structural equations (LMS), and bias-corrected bootstrap procedures. Results: Positive peer career support was positively associated with career calling (β = 0.31, p < 0.001), whereas negative peer career pressure was negatively associated with career calling (β = 0.27, p < 0.001); only negative peer pressure showed a direct association with career indecision. Career calling was negatively associated with career indecision (β = 0.46, p < 0.001) and fully mediated the link between positive peer support and career indecision, while partially mediating the link between negative peer pressure and career indecision. Internalized prosocial values moderated the association between negative peer pressure and career calling (interaction β = 0.18, p < 0.001); the conditional indirect association of negative peer pressure was substantially attenuated at high levels of value internalization. Discussion: These findings extend SCCT by showing that internalized prosocial values may serve as a contextual protective factor that attenuates the maladaptive association of peer pressure with meaning-based career development, and highlight the importance of distinguishing supportive from pressuring peer dynamics in understanding career decision-making among emerging adults in a collectivist cultural context. Given the cross-sectional design, longitudinal replication is needed to corroborate the proposed directional relationships.

Tightly coupled LiDAR-visual-inertial simultaneous localization and mapping (simultaneous localization and mapping, SLAM) systems are prone to observation distortion, weak state observability, and cumulative drift under weak-texture conditions, dynamic illumination, geometric degradation, multisensor time asynchrony, and long-term operation. To address these issues, we propose a spatiotemporal consistency enhancement method. The method is built upon the sequentially updated Error-State Iterated Kalman Filter (ESIKF) framework of FAST-LIVO2. First, using the Inertial Measurement Unit (IMU) clock as the unified reference, the LiDAR–IMU and Camera–IMU time offsets are jointly estimated online, and the estimates are fed back to point-cloud motion compensation and image reprojection. Second, through joint parameterization of vignetting, exposure time, and the camera response function, pixel intensity is inversely mapped to scene irradiance, while scale normalization and physical feasibility constraints are introduced to improve the identifiability of photometric parameters. Third, a LiDAR health indicator is established from the spectrum of a scale-normalized Hessian matrix, and direction-selective kinematic virtual observations are introduced under degeneration when motion is in a steady state and the innovation passes the gating threshold. Finally, low-frequency loop-closure constraints are generated using a ring-sector descriptor, rotation-invariant matching, and point-cloud geometric verification, while small-residual gating and progressive correction are adopted to reduce violation of the small-error assumption of ESIKF. Experimental results show that the proposed method achieves an overall RMSE of 0.379 m on the self-built visual and geometric degradation datasets; on the building-corridor loop-closure sequence, the average ATE decreases from 0.188 m with FAST-LIVO2 to 0.080 m; and in the unified ablation experiment, the average RMSE of the full system decreases from 0.153 m with FAST-LIVO2 to 0.117 m, a reduction of 23.5%. These results indicate that the enhancement modules are complementary in improving multisource observation consistency, degradation robustness, and long-term drift suppression.