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Efficient dynamic programming in economical cruise control under real-time traffic situations

In this paper, dynamic programming (DP) economical cruise control algorithms under discrete distance and discrete time strategies are designed to improve battery electric vehicle (BEV) energy consumption and riding comfort. In our experiment, energy consumption of constant-speed driving is reduced by up to 21.6% using the optimum velocity profiles from discrete distance DP. ‘Variable step length and boundary conditions’ are utilized to reduce discrete distance DP processing time by 90.8% with no effect on accuracy. Under discrete time DP, position is introduced as an additional variable, so discrete time DP allows more inputs including real-time preceding vehicle position and traffic information. The experiment results indicate that under traffic situation, discrete time DP further improves energy consumption and riding comfort by avoiding unnecessary stop at intersections compared with discrete distance DP. Further analysis indicates that under properly controlled velocity, the transfer between potential energy and kinetic energy is more efficient than that between potential energy and electric energy. These DP economical cruise control algorithms are effective for BEVs, hybrid electric vehicles and plug-in hybrid electric vehicles. ‘Variable step length and boundary conditions’ method in DP can also be applied in other domains where DP processing time is crucial.