This paper proposes a dual-mode AFE system that supports both voltage and electrochemical impedance spectroscopy (EIS) monitoring for multiple cell Li-ion batteries. The proposed current-balanced IA adapts the common mode voltage of the selected cell by taking the same voltage from the same cell, enabling multiple cell monitoring with minimum current. The system also includes a DC-servo loop cancelling the DC component from the AC voltage excited by a current generator. To the best of the author’s knowledge, it is the first BMS AFE system supporting multiple cell EIS monitoring, providing better SoC/SoH estimation and safety for Li-ion batteries.

Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.