To guarantee the safety of electric vehicles (EVs), abusive operations should be strictly prohibited for EV-mounted li-ion batteries (LiBs). This article proposes an intelligent strategy to infer the historical abusive operations (HAOs) on LiBs by retrospectively analyzing the relationship between HAO-induced damages and electrical behaviors. First, the electrical synchronicity among the peer LiB cells in a series module is perceived using an improved correlation coefficient formula; then the synchronicity sequences are translated into recurrence plot images (RCPIs) and Gramian angular field images (GAFIs) that can provide a wealth of textures regarding cross-time autocorrelations. Second, based on the hierarchical clustering algorithm, a pilot test is performed to preliminarily examine the separability of these images corresponding to different HAOs. Finally, the GoogLeNet model is employed to model the causality between image features and HAO specifics, whereby the type and intensity of potential HAO can be inferred. A realistic dataset is obtained by inflicting abuses such as overvoltage, overheat, and vibration on LiB cells. Experimental verifications show that the proposed strategy performs well in backtracking the HAOs on LiBs by providing effective and reliable inferences on HAO specifics. The accuracy rates of HAO type assessment and severity evaluation can achieve about 77% and 76% using RCPIs, and about 79% and 76% using GAFIs, respectively.

Resistance coefficient (RC) is an important measure when designing wheel-driving mechanisms and accurate dynamic models for real-time mobility control of wheeled mobile robots (WMRs). This measure is typically formulated as a constant that depends on the wheel load, wheel dimensions, and soil that the WMR is designed for. This article proposes a novel variable RC that responds to terrain deformation. This variable RC is then applied to controllers for WMRs that estimate driving torques and slip ratios on deformable terrain. Simple yet accurate models of RC are developed from both experimental results and theoretical analysis, and these models are then compared with other methods. The proposed RC models give more accurate and more computationally efficient estimations of driving torques and slip ratios for WMRs, with average estimation errors less than 6% and the shortest computation time in experiments. The two proposed estimators are then applied to the design of the tracking-control systems for a WMR running on deformable terrain. Experiments with simulated sandy terrain demonstrate that both proposed control systems are feasible, and the slip estimation effectively decreases velocity tracking errors from more than 20% to less than 10%.