A two-level stochastic approach to optimize the energy management strategy for fixed-route hybrid electric vehicles

Zeng, Xiangrui; Wang, Junmin

doi:10.1016/j.mechatronics.2015.11.011

Cited by 41 publications

(13 citation statements)

References 19 publications

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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%

“…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%

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On the Optimal Speed Profile for Electric Vehicles

Gruber

Tavernini

et al. 2020

IEEE Access

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The main question in eco-driving is-what speed or torque profile should the vehicle follow to minimize its energy consumption over a certain distance within a desired trip time? Various techniques to obtain globally optimal energy-efficient driving profiles have been proposed in the literature, involving optimization algorithms such as dynamic programming (DP) or sequential quadratic programming. However, these methods are difficult to implement on real vehicles due to their significant computational requirements and the need for precise a-priori knowledge of the scenario ahead. Although many predictions state that electric vehicles (EVs) represent the future of mobility, the literature lacks a realistic analysis of optimal driving profiles for EVs. This paper attempts to address the gap by providing optimal solutions obtained from DP for a variety of trip times, which are compared with simple intuitive speed profiles. For a case study EV, the results show that the DP solutions involve forms of Pulse-and-Glide (PnG) at high frequency. Hence, detailed investigations are performed to: i) prove the optimality conditions of PnG for EVs; ii) show its practical use, based on realistic electric powertrain efficiency maps; iii) propose rules for lower frequency PnG operation; and iv) use PnG to track generic speed profiles. INDEX TERMS Dynamic programming, eco-driving, electric vehicles, speed profile, optimization, pulse-and-glide.

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Section: B Comparison Of Opt-cppt and Dp Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Optimal Speed Profile for Electric Vehicles

Gruber

Tavernini

et al. 2020

IEEE Access

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show abstract

“…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%

Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

et al. 2019

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A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred to as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period-to-period improvements. Its performances will approach dynamic programming (DP)based method after a learning process with satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV. INDEX TERMS Plug-in hybrid electric vehicle, model predictive iterative learning control, battery aging, nonlinear optimization, 2-D Lyapunov stability theory.

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“…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%

Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.

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A two-level stochastic approach to optimize the energy management strategy for fixed-route hybrid electric vehicles

Cited by 41 publications

References 19 publications

On the Optimal Speed Profile for Electric Vehicles

On the Optimal Speed Profile for Electric Vehicles

Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction

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