To improve energy utilization efficiency for fuel cell vehicles (FCVs), it is crucial to pay particular attention to the coordination relationships between speed planning and energy management on the road with multipl...
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To improve energy utilization efficiency for fuel cell vehicles (FCVs), it is crucial to pay particular attention to the coordination relationships between speed planning and energy management on the road with multiple signal lights. However, the coordination relationships are complex optimization problems with large computational loads, which make it challenging to consider speed planning and energy management simultaneously. Then, a co-optimization method based on Pontryagin's minimum principle (PMP) with the cooperative optimization algorithm is proposed for speed planning and energy management in the FCVs. An optimization control model for the speed planning problem online is established using the signal light timing model and solved using the PMP method to reduce the number of accelerations and decelerations, get closer to the desired speed, and decrease the demand power. Meanwhile, the optimization objective function for energy management is built to achieve the minimum hydrogen consumption, and the PMP method is re-applied to achieve a reasonable distribution of the output power of the fuel cell and lithium battery. Additionally, a cooperative optimization algorithm by conjunct use of the Whale optimizationalgorithm (WOA) and the Grey wolf optimization (GWO) is put forward to optimize the parameters in the PMP method. The simulation results indicate that the proposed method avoids waiting at red lights during the vehicle speed planning phase, minimizes speed fluctuations, and improves driving comfort. Moreover, compared to the Quadratic Programming (QP), when this method is utilized for power distribution between two sources, the FCHEV consumes 7.51% less hydrogen in route one and 1.35% less hydrogen in route 2. Besides, the method yields a more stable output power from the fuel cell. In summary, the method proposed in this paper fulfills the real-time optimization requirements for speed and energy.
In this paper we introduce an iterative distributed Jacobi algorithm for solving convex optimization problems, which is motivated by distributed model predictive control (MPC) for linear time-invariant systems. Starti...
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In this paper we introduce an iterative distributed Jacobi algorithm for solving convex optimization problems, which is motivated by distributed model predictive control (MPC) for linear time-invariant systems. Starting from a given feasible initial guess, the algorithm iteratively improves the value of the cost function with guaranteed feasible solutions at every iteration step, and is thus suitable for MPC applications in which hard constraints are important. The proposed iterative approach involves solving local optimization problems consisting of only few subsystems, depending on the flexible choice of decomposition and the sparsity structure of the couplings. This makes our approach more applicable to situations where the number of subsystems is large, the coupling is sparse, and local communication is available. We also provide a method for checking a posteriori centralized optimality of the converging solution, using comparison between Lagrange multipliers of the local problems. Furthermore, a theoretical result on convergence to optimality for a particular distributed setting is also provided. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
In this paper we introduce an iterative distributed Jacobi algorithm for solving convex optimization problems, which is motivated by distributed model predictive control (MPC) for linear time-invariant systems. Starti...
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