Hybrid-Trip-Model-Based Energy Management of a PHEV With Computation-Optimized Dynamic Programming

Liu, Jichao; Chen, Yangzhou; Li, Wei; Shang, Fei; Zhan, Jingyuan

doi:10.1109/tvt.2017.2777852

Cited by 69 publications

(28 citation statements)

References 26 publications

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“…The global optimization energy management strategy was put forward for the first time because of the best optimization results [6]. Liu et al applied dynamic programming (DP) algorithm to the hybrid trip mode issues and results indicated fuel consumption decreased [7]. Other than DP algorithm, Murgovski et al used convex optimization techniques to solve Pontryagin's maximum principle and achieved good performance [8].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

Jiao

Chao

et al. 2020

IEEE Access

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Vehicle-following operation is a typical scenario in the future intelligent transportation environment. Keeping a safe distance is the most important goal in the vehicle-following scenario. For a plug-in hybrid electric bus (PHEB) running in a specific urban route, the challenge will become how to realize the optimal power split in hybrid powertrain under the premise of maintaining driving safety. Considering the above issues, this paper proposes a stochastic model predictive control (SMPC) strategy for PHEBs during vehicle-following scenario. Firstly, Markov chain-based stochastic driving model is built using real-world bus driving condition data, which is applied to predict future demand torque over a finite receding horizon. And then, a sequential quadratic programming (SQP) algorithm is adopted to solve the rolling optimization problem. Meanwhile, brake specific fuel consumption and electric motor efficiency are fitted offline to match the SMPC strategy. Furthermore, a piecewise function is given to adjust the adaptive factor that balancing fuel economy and vehicle-following in the pre-set cost function. Finally, to verify the control performance of the proposed strategy, a nonlinear model predictive control strategy with dynamic programming optimization (DP-MPC) and a rule-based (RB) strategy are employed for comparison study. Results indicate that the proposed strategy is effectiveness to the given driving condition with excellent fuel economy and vehicle-following performance. Under the driving condition of Chongqing 303 bus line in China and China typical, the fuel consumption is reduced by 20.58% and 37.89% compared with RB strategy, respectively. It is closer to the fuel consumption reduction of 16.77% and 13.11 optimized by DP-MPC. Driving safety during vehicle-following also be demonstrated in the driving condition of Chongqing 303 bus line and China typical. INDEX TERMS Plug-in hybrid electric bus, energy management, vehicle-following, stochastic model predictive control, sequential quadratic programming optimization.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

Jiao

Chao

et al. 2020

IEEE Access

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“…In comparison with fuel consumption, electricity consumption has the advantages of low price and zeroemission when used to drive the same distance [44]. Thus, the optimization goal is to minimize fuel consumption by consuming electric power until the PHEV reaches the destination.…”

Section: ) Identify the Problems To Be Optimized And Divide The Optimization Process Into Stagesmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic "Engine optimal operating curve control" strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.INDEX TERMS Plug-in hybrid electric vehicle, dynamic programming, random forest, Markov chain, wavelet transform.

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“…Many optimal control methods have been adopted to develop suitable blending strategies. According to the various optimal horizons, these optimal control methods can be divided into two types [12]: global and instantaneous optimization. Dynamic programming (DP) [13] and Pontryagin's minimum principle (PMP) [14] are the classic examples of global optimization strategies.…”

Section: Dmentioning

confidence: 99%

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

et al. 2019

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This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

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Hybrid-Trip-Model-Based Energy Management of a PHEV With Computation-Optimized Dynamic Programming

Cited by 69 publications

References 26 publications

An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

An Adaptive Stochastic Model Predictive Control Strategy for Plug-in Hybrid Electric Bus During Vehicle-Following Scenario

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

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