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As a global optimization method, dynamic programming (DP) can be employed to seek the optimal velocity with minimum energy consumption for EV on given driving cycles. Due to its terrible computational burden, conventional DP is not suitable for real-time implementation especially with higher dimensions. In this paper, we propose an iterative dynamic programming (IDP) approach to reduce computing time firstly. The IDP can obtain the optimal control laws alike the conventional DP by converging the optimal control strategy iteratively and save considerable computing time. Second, the developed IDP and model predictive control (MPC) are combined to establish a real-time cruising controller called IDP-MPC for an EV with terrain preview. In the predictive controller, we use the IDP to solve a constrained finite horizon nonlinear optimization problem.

The design of aluminum foam reinforced thin-walled tubes has garnered much interest recently due to the high energy absorption capacity of these tubes. As a new kind of engineering composite material, aluminum foam can hugely increase the crashworthiness capacity without sacrificing too much weight. In this paper, axisymmetric thin-walled hollow tubes with four different kinds of cross-sections (circular, square, hexagonal and octagonal) are studied to assess their performance for crashworthiness problems. It is found that the tube with square cross-section has the best crashworthiness performance under axial impact. To seek optimal designs of square aluminum foam reinforced thin-walled tubes, a surrogate modeling technique coupled with a multi-criteria particle swarm optimization algorithm has been developed, to maximize specific energy absorption (SEA) and minimize peak crash force (PCF).