Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme.

The emergence of all-solid-state Li batteries (ASSLBs) represents a promising avenue to address critical concerns like safety and energy density limitations inherent in current Li-ion batteries. Solid electrolytes (SEs) show significant potential in curtailing Li dendrite intrusion, acting as natural barriers against short circuits. However, the substantial challenges at the SEs−electrode interface, particularly concerning the anode, pose significant impediments to the practical implementation of ASSLBs. This review aims to delineate the most viable strategies for overcoming anode interfacial hurdles across four distinct categories of SEs: sulfide SEs, oxide SEs, polymer SEs, and halide SEs. Initially, pivotal issues such as anode interfacial side reactions, inadequate physical contact, and Li dendrite formation are comprehensively outlined. Furthermore, effective methodologies aimed at enhancing anode interfacial stability are expounded, encompassing approaches like solid electrolyte interface (SEI) interlayer insertion, SE optimization, and the adoption of Li alloy in lieu of Li metal, each tailored to specific SE categories. Moreover, this review presents novel insights into fostering interfaces between diverse SE types and Li anodes, while also advocating perspectives and recommendations for the future advancement of ASSLBs. Graphical Abstract

Battery fast charging strategies have gained an increasing interest towards the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifespan, and charging time, this paper proposes a novel health-aware multi-objective optimal charging strategy to simultaneously shorten the charging time and relieve the battery degradation. The multi-objective optimal charging problem is formulated based on a coupled electrochemical-thermal-aging battery model. Constraints are explicitly imposed on physically meaningful state variables to avoid hazardous operations. Charging duration and battery aging process are well traded-off. Strategies for minimum-time and health-aware fast charging are investigated using different input current bounds, subject to both side reaction and temperature constraints. The experimental results validate that the presented multi-objective health-aware optimal charging algorithm is capable of reducing the charging time from its benchmarks largely without sacrificing the state-of-health of the battery.

In this article, path planning for intelligent vehicle collision avoidance of dynamic pedestrian using attention mechanism-long short-term memory network (Att-LSTM), modified social force model (MSFM), and model predictive control (MPC) is systematically investigated, and pedestrian-dynamic vehicle conflict scene at an unsignalized crosswalk is covered. First, a data-driven stacking fusion model based on the Att-LSTM and MSFM is proposed for pedestrian path prediction. Pedestrian heterogeneity (age and gender) is taken into account for the first time. The data-driven stacking fusion model is verified with the existing methods. Then an MPC-based path planning-tracking system considering pedestrian path prediction is developed. The predicted path of pedestrian is treated as a pedestrian-safety region with the combination of a semiellipse and semicircle, and the front wheel steering angle is calculated to prevent the intelligent vehicle from colliding with the dynamic pedestrians. Simulink–Carsim simulations are presented to simulate the real dynamic environment where crossing pedestrians exist. The results illustrate that the developed MPC system considering pedestrian path prediction can provide dynamic path planning performance acceptably and effectively, and make it possible for the intelligent vehicle to present the great feasibility for pedestrian safety protection and traffic efficiency improvement.