Variable horizon-based predictive energy management strategy for plug-in hybrid electric vehicles and determination of a suitable predictive horizon

For plug-in hybrid electric vehicles, the predictive energy management can be implemented in real-time with a short-term prediction of future driving condition. However, the completely precise prediction of future driving conditions and the determination of a reasonable predictive horizon remain quite difficult. To solve the above problems, we propose a variable horizon-based predictive energy management control method with adaptive SOC constraint, and more importantly, a reasonable predictive horizon is determined to balance global energy optimality and the cost of data sampling for the connected vehicles. Firstly, an integrated velocity predictor is proposed by combining Markov model and speed limits. Considering that the effect of information amount on prediction accuracy is opposite to that of predictive horizon, a new evaluation metric, namely predictive information entropy, is proposed to objectively evaluate the velocity prediction accuracy under different amounts of available information. Then, a variable horizon-based predictive energy management strategy is developed with the dynamically updated reference SOC. By comprehensively considering the effects of predictive horizon on prediction accuracy, real-time application and global optimality, a suitable predictive horizon should be set to 20s–30s to achieve a better fuel economy with a relatively low data sampling cost.