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

Abstract

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.

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

“…Liu et al designed a novel energy management strategy based on velocity prediction and reinforcement, and the results demonstrated the feasibility of reducing fuel consumption [16]. Zhang et al employed model predictive control (MPC) method to calculate the optimal power distribution, which proved the fuel economy improvement in simulation environment [17]. Among the above all control methods, MPC shows excellent optimization performance and many scholars are attracted to focus on that [18], [19].…”

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

“…The new directions of EVs on actual research studies from 2019 to 2020 are the following: “battery electric vehicles with zero emission, improving lithium‐ion battery energy storage density, safety, and renewable energy conversion efficiency,” 103 “traffic congestion and the waiting time at charging stations, achieved Nash equilibrium substantially improves the load balance across the grid,” 104 “large companies whose supply chain may involve hundreds of commercial vehicles, costs,” 105 “vehicle's acceleration process by controlling the driving behavior, pedal control strategy, updated algorithms,” 106 “bidirectional charging, facilitating islanding and cost‐effective management of main grid use,” 107 “energy‐efficient powertrain requires tackling several conflicting control objectives such as the drivability, fuel economy, reduced emissions, and battery state of charge preservation,” 97 “comprehensive technical review for pure electric vehicles,” 108 “increase the autonomy of the vehicle, as a good self‐ dispatch energy system,” 109 “increase the autonomy of the vehicle, a good self‐dispatch energy system,” 110 “effective approach of DSM based on predetermined hourly generation and time‐varying tariffs to enhance the reliability and quality of a stand‐alone energy system,” 111 “reduce the battery life degradation, battery degradation cost and the electric cost, reduce the energy losses, and handle the system constraints,” 25 “reduce charging waiting time and efficiently design driving behaviors from spots to charging stations, bi‐functional charging management strategy,” 112 “fuel consumption to noise emissions up to battery aging and engine start‐up costs,” 113 “bi‐level online energy management for a battery‐based fuel cell electric vehicle based on operational mode control,” 114 “DPR ‐ wavelet transform‐fuzzy logic control energy management strategy based on driving pattern recognition,” 115 “wavelet transform, neural network and fuzzy logic,” 116 “system constraints, cost function of the model predictive control,” 117 “Improved fuel economy and SOC charge sustainability,” 118 “supervisory control strategy, control framework implementing Model‐based Q‐learning,” 119 “minimize the energy consumption in unknown driving cycles,” 120 “mixed‐integer nonlinear optimal control problem, hierarchical supervisory control architecture,” 121 “DRL's advantages of requiring no future driving information in derivation and good generalization in solving energy management problem,” 122 “fixed models of power sources energy consumption and efficiency,” 123 “multimode power‐split, increased flexibility, predicted fuel consumption and computational cost,” 124 “state‐of‐charge and state‐of‐power capability joint estimator, quantifiable battery degradation model,” 125 “fast rolling optimization for plug‐in hy...…”

Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles

“…In Xu et al [37], an EMS based on an NN algorithm to optimise the torque and speed for electrical variable transmission (EVT)-HEV and the efficiency optimization strategy of a regenerative braking system was presented. In Zhang et al [38], an MPC, DP and synthesised velocity profile prediction to optimise the driving conditions and SoC for parallel-PHEV were proposed.…”