An Enhanced Approach for Load Behavior and Battery Residual Capacity Prediction Using Markov Chain and Monte Carlo Method

The demands for energy storage systems and energy management systems are increasing with the development of modern electric vehicles. The battery management technology is the basis and key to ensure vehicle safety and prolong the battery life of electrified vehicles. One of the technical bottlenecks in battery management technology is the state estimation of the power batteries. Because the behavior of the lithium-ion battery is highly nonlinear, and the internal states and model parameters are susceptible to temperature, noise, and other factors, the battery states are difficult to be observed accurately. In the past decades, many battery state estimation strategies have been developed. However, dynamic systems in the real world, whether linear or nonlinear, usually exhibit behavior that leads to excessive outliers. This article attempts to discuss the coestimation method for state-of-charge and remaining discharge time. First, a temperature-compensated model is presented based on the equivalent-circuit model. Then, the Bayesian estimation method is employed for the coestimation of state-of-charge and remaining discharge time. Moreover, the Markov chain prediction model is presented for the load profiles. Experimental studies showed that the proposed methods have accurate and robust state-of-charge estimates under dynamic driving cycles with mean absolute error less than 2%.