The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

The mapping between open circuit voltage (OCV) and state of charge (SOC) is critical to the lithium-ion battery management system (BMS) for electric vehicles. In order to solve the poor accuracy in the local SOC range of most OCV models, an OCV model fusion method for SOC estimation is proposed. According to the characteristics of the experimental OCV–SOC curve, the method divides SOC interval (0, 100%) into several sub-intervals, and respectively fits the OCV curve segments in each sub-interval to obtain a corresponding number of OCV sub-models with local high precision. After that, the OCV sub-models are fused through the continuous weight function to obtain fusional OCV model. Regarding the OCV curve obtained from low-current OCV test as the criterion, the fusional OCV models of LiNiMnCoO₂ (NMC) and LiFePO₄ (LFP) are compared separately with the conventional OCV models. The comparison shows great fitting accuracy of the fusional OCV model. Furthermore, the adaptive cubature Kalman filter (ACKF) is utilized to estimate SOC and capacity under a dynamic stress test (DST) at different temperatures. The experimental results show that the fusional OCV model can effectively track the performance of the OCV–SOC curve model.

The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its complexity. However, it is difficult to capture the aging behavior of batteries, and the coupling mechanism between multiple scales is still incomplete. In this paper, a simplified electrochemical model (SEM) and a kinetic Monte Carlo (KMC)-based solid electrolyte interphase (SEI) film growth model are used to study the multi-scale characteristics of LIBs. The single-particle SEM (SP-SEM) is described for macro scale, and a simple and self-consistent multi-particle SEM (MP-SEM) is developed. Then, the KMC-based SEI model is established for micro-scale molecular evolution. And, the two models are coupled to construct the full-cycle multi-scale model. After modeling, validation is performed by using a commercial 18650-type LIB. Finally, the effect of parameters on the SEI model is studied, including qualitative trend analysis and quantitative sensitivity analysis. The growth of SEI film with different particle sizes is studied by MP-SEM coupling simulation.

With the aggravation of environmental pollution and energy crisis, lithium-ion batteries are widely regarded as promising. However, the current distribution in the parallel battery pack branches is highly heterogeneous. Charging strategies based on the models can be adopted to prevent side reactions that may lead to severe degradation or even thermal runaway under various ambient temperatures. In this study, a battery model for a single cell is established by coupling a single particle model with electrolyte, degradation model, and thermal model. Besides, considering the contact resistance and wire resistance, the circuit model of a battery pack is established. A charging strategy based on minimum Li plating overpotential control is then adopted, and the effectiveness under high C-rate and low temperature to reduce capacity loss is verified by simulation. This study provides a low-loss charging strategy that can reduce the safety risk of battery packs with better performance under various ambient temperatures.