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Cited by 92 publications
(43 citation statements)
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“…Other optimization strategies used in the literature are often categorized as direct and indirect optimal control approaches. Direct approaches transform the optimization problem into a nonlinear programming (NLP) problem and have been used substantially as SCS in HEVs [20,21], due to the availability of many well-established NLP solvers. As for indirect methods they are solved as a boundary value problem resulting from the Pontryagin Minimum Principle (PMP) [22].…”
Section: Introductionmentioning
confidence: 99%
“…Other optimization strategies used in the literature are often categorized as direct and indirect optimal control approaches. Direct approaches transform the optimization problem into a nonlinear programming (NLP) problem and have been used substantially as SCS in HEVs [20,21], due to the availability of many well-established NLP solvers. As for indirect methods they are solved as a boundary value problem resulting from the Pontryagin Minimum Principle (PMP) [22].…”
Section: Introductionmentioning
confidence: 99%
“…Planetary gear (PG) units enable various operating modes (parallel, series and power-split modes) by switching and/or controlling the clutches and energy devices, thus enhancing the energy efficiency of the powertrain [2,3]. Meanwhile, the energy management problem of HEVs has been tackled by diverse approaches, such as dynamic programming (DP)-based strategies [4][5][6][7][8], model predictive control (MPC)-based design methods [9][10][11], stochastic MPC-based approaches [12,13], and logical dynamic system theory-based approaches [14]. Indeed, energy management has been a focal research topic for over two decades, and is usually investigated in dynamical models of the battery and the powertrain.…”
Section: Introductionmentioning
confidence: 99%
“…The energy management of HEVs with PG units has been solved using a DP algorithm, providing a benchmark optimal solution [4][5][6][7]. Meanwhile, MPC-based methods [9,10] have provided optimal solutions in real-time applications. In most contributions [4][5][6][7][8][9][10], the demand power distribution has been realized through model-based control design.…”
Section: Introductionmentioning
confidence: 99%
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“…In order to improve the fuel economy of the vehicle, a wide range of optimal control methods has been proposed to solve the energy management problem of PHEVs. Examples include rule-based control [1], deterministic dynamic programming [2], stochastic dynamic programming [3], the equivalent consumption minimization strategy (ECMS) [4], Pontryagin's minimum principle [5] and model predictive control [6]. Furthermore, many intelligent optimization algorithms have been used for solving the energy management control problem recently [7,8].…”
Section: Introductionmentioning
confidence: 99%