Model Predictive Charging Control of In-Vehicle Batteries for Home Energy Management Based on Vehicle State Prediction

Thanks to recent development of reciprocal communication networks and electric power management infrastructure, an energy management system, which can automatically regulate supply-demand imbalances under conditions of the users' convenience and economy, is attracting great attention. On the other hand, finding of new usage of the batteries employed in electric vehicles and plug-in hybrid vehicles is recognized as one of key issues to realize the sustainable society. In addition, development of vehicle to X technology enables us to use the electric power of in-vehicle batteries for various purposes. Based on these backgrounds, this paper presents an integrated strategy for charging control of in-vehicle batteries that optimizes the charge/discharge of in-vehicle batteries in a receding horizon manner exploiting the predicted information on home power load and future vehicle state in the household. The prediction algorithm of future vehicle state is developed based on semi-Markov model and dynamic programming. In addition, it can also be implemented in receding horizon manner, i.e., the predicted vehicle state is updated at every control cycle based on the new observation. Thus, the harmonious combination of stochastic modeling/prediction and MPC in real-time home energy management system is one of the main contributions of this paper. Effectiveness of the proposed charging control is demonstrated by using an experimental testbed.