This article provides a comprehensive overview of the potential challenges and solutions of second-life batteries. First, safety issues of second-life batteries are investigated, which is highly related to the thermal runaway of battery systems. The critical solutions for the thermal runaway problem are discussed, including structural optimization, parameter identification, advanced BMS, and artificial intelligence (AI)-based control strategies. Furthermore, the cell inhomogeneity problem of second-life battery systems is analyzed, where the passive balancing strategy and active balancing strategy are reviewed, respectively. Then, the compatibility issue of second-life batteries is investigated to determine whether electrical dynamic characteristics of a second-life battery can meet the performance requirements for energy storage. In addition, date security and protection methods are reviewed, including digital passport, smart meters and Internet of Things (IoT). The future trends and solutions of key challenges for second-life battery utilization are discussed.

This paper proposes a phase current reconstruction strategy based on a dc bus using a single current sensor for a surface permanent magnet synchronous motor (SPMSM). The method of a single current sensor reduces the number of mechanical hall sensors and shunt resistors by using a modified current reconstruction algorithm. The information of rotor position is estimated by the sliding mode observer for its rapid response and strong anti-interference ability, and the observer needs to detect voltage and current components from α − β coordinate system. In order to reduce the buffeting problem of sliding mode observers, an adaptive neural network is introduced, by the way of extracting angle speed estimated values from sliding mode observers, and these values are trained to obtain the compensate angular velocity and minus index value to suppress speed value. The performance of this sensorless speed regulation strategy in the high-speed region using a single current sensor with an optimized adaptive neural network is verified and evaluated by PSIM simulation and experiments.