2016
DOI: 10.1016/j.mechatronics.2015.11.011
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A two-level stochastic approach to optimize the energy management strategy for fixed-route hybrid electric vehicles

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Cited by 41 publications
(13 citation statements)
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“…where the constraints are the same as in (12), and the additional term β |T m (k) − T m (k − 1)| penalizes the torque oscillations. In the modified DP, both v and the motor torque T m are states, as the previous torque value, T m (k − 1), must be available to evaluate V opt .…”
Section: B Comparison Of Opt-cppt and Dp Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…where the constraints are the same as in (12), and the additional term β |T m (k) − T m (k − 1)| penalizes the torque oscillations. In the modified DP, both v and the motor torque T m are states, as the previous torque value, T m (k − 1), must be available to evaluate V opt .…”
Section: B Comparison Of Opt-cppt and Dp Resultsmentioning
confidence: 99%
“…Learning algorithms have been developed for vehicles traveling on fixed routes; for example, Kim et al [11] use a learning model predictive controller (MPC) to iteratively improve the fuel economy of an ICE vehicle. Another technique suitable for fixed routes is stochastic DP, which accounts for uncertainties in historical speed data, and generates a look-up table for future use, as presented for an HEV in [12].…”
Section: Introductionmentioning
confidence: 99%
“…Because the vehicles (including passenger cars and commercial vehicles) usually run in fixed route which has iterative repetitiveness of the route and similar terrain of a certain region, the iteration-varying feature of driver behavior can be obtained from the historical driving data. Inspired by the method [43] and according to the iterative repetitiveness of the city route, an important assumption should be given first for the MPILC-based EMS. Assumption 1: There exists an intrinsical characteristics of the iteration-varying features which can be defined by HOIM and are not limited to the cases on the same city route including: different vehicle, different driver, new vehicle or new driver operated on the route first.…”
Section: Optimal Control Problem Formulation and Solutionmentioning
confidence: 99%
“…5,6 On the basis of the Bellman optimal control method, DP can take the fuel economy and emission level as the performance index of the optimal control under known driving conditions. 7 DP can also take the related performance parameters as the constraints to solve a multistage decision problem in the fixed cycle and finally obtain the optimal control law. However, the computation is extremely large and not applicable to real-time controllers directly.…”
Section: Introductionmentioning
confidence: 99%