Although the fuzzy logic control strategy is good at dealing with model uncertainty and complex decisions, the formulation of its Optimal control of phev problem formulation rules is lack of system approach and mainly depends on engineering experience, which leads to loss of control accuracy [ 78 ].

Due to the small computational time, near-optimal characteristics, and the feasibility of online implementation, ECMS has widely been used to address the energy management control problem for both HEVs [ 1718 ] and PHEVs [ 1920 ]. Vehicle Dynamics Models Based on the wheel force balance, vehicle dynamics models must give power balance equation in every time step of simulation computation.

Introduction Hybrid electric vehicles HEVs are most promising among all the new energy vehicles including battery electric vehicles and fuel cell vehicles to better fuel economy and emissions without compromising vehicle performances [ 12 ].

In this paper, a real-time control strategy with ECMS for a PHEV is proposed, which is based on a new method for evaluating the equivalent factor between fuel and electrical energy in order to regulate SOC at a constant reference point with the minimum fuel consumption simultaneously.

Thus, battery discharging at any time is equivalent to fuel consumption of the ICE in the future. However, due to computational complexity, it is not easily implemented for practical applications. This method can be a good analysis and assessment tool for other control strategies. Section 2 introduces the vehicle configuration and models of the parallel hybrid electric vehicle.

Optimal control of phev problem formulation more promising approach of the real-time optimization is used in [ 16 ], which is defined as equivalent consumption minimization strategy ECMS.

The real-time control strategy is based on instantaneous optimization and defines a cost function which is guaranteed to be minimum at each instant depending upon system current variables.

Due to the causal nature of global optimization technique, it is not suitable for real-time analysis, because the main aim of the real-time analysis is to reduce global criterion to an instantaneous optimization by introducing a cost function that depends only on the present state of the system parameters [ 1112 ].

Vehicle and assembly models of PHEVs are established, which provide the foundation for the following calculations. The key problem of the ECMS design is the calculation of the equivalent factor between fuel and electrical energy based on the available vehicle information, because it has a major influence on the fuel economy and the charge sustainability of PHEVs.

Based on the models established of the PHEV, computation and optimization of the total equivalent fuel consumption are discussed in detail in the paper. The remainder of this paper is organized as follows. Schematic diagram of the PHEV. The real-time optimal control strategy is designed through regarding the minimum of the total equivalent fuel consumption as the control objective and the torque split factor as the control variable.

Dynamics equation is described by the following: This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Hence, in order to derive cost functions for instantaneous optimization of power split, while maintaining batteryreal-time optimization is performed. There are many methods to improve fuel economy of HEVs, such as optimizing their mechanical construction, matching the powertrain parameters, and lighting the body.

The main component specifications of the hybrid powertrain system are listed in Table 1. The ECMS is described in detail, in which an instantaneous cost function including the fuel energy and the electrical energy is proposed, whose emphasis is the computation of the equivalent factor.

Although this strategy can offer a prominent improvement in energy efficiency and is also adopted widely in the commercial HEV, it is clear that the strategy does not guarantee an optimal value in all cases or allow the vehicle to run at maximum efficiency when the parameters are fixed [ 56 ].

Moreover, global optimization technique does not consider variations of battery in the problem. This paper will optimize the energy management strategy, which distributes the total torque demanded at wheels between the ICE and the electric motor EM to minimize the fuel consumption and maintain the battery state of charge SOC simultaneously.

Abstract A real-time optimal control of parallel hybrid electric vehicles PHEVs with the equivalent consumption minimization strategy ECMS is presented in this paper, whose purpose is to achieve the total equivalent fuel consumption minimization and to maintain the battery state of charge within its operation range at all times simultaneously.

Models of the PHEV 2. To be distinguished, the real-time control strategy without ECMS is defined as the simpler real-time strategy. The papers [ 910 ] propose a global optimal strategy based on dynamic programming methods for parallel hybrid electric vehicles PHEVs and parallel-series HEV, respectively.

Therefore, they are composed of dynamics equation and power balance equation. Section 3 then describes the novel ECMS algorithm. Both the output torque of the ICE and the EM are coupled by the torque coupling mechanism TCMwhose output torque is then transmitted into the gearbox and final drive, through which the vehicle is ultimately propelled.

The design of the real-time optimal control is presented in Section 4. Various attempts have been made to propose real-time control based on instantaneous optimization [ 13 — 15 ].

Many energy management strategies have been proposed for efficient energy usage, which can be classified into four types, namely, the rule-based control strategy, the global optimal strategy, the real-time optimization control strategy, and the fuzzy logic control strategy.

Validation of the control strategy proposed and optimization results are discussed in Section 5. The rule-based control strategy sets the initial value of the parameters by mostly relying on engineering experience and then adjusts these parameters by adopting the trial-and-error method. In the last few decades, many automobile manufacturers have been researching HEVs and have obtained several configurations for practical applications [ 34 ].A STOCHASTIC OPTIMAL CONTROL APPROACH FOR POWER MANAGEMENT IN PLUG-IN HYBRID ELECTRIC VEHICLE S 2.

PROBLEM FORMULATION The above control inputs affect the PHEV plant by a ffecting its state variables. In this paper, we closely fol low some of the.

Optimal Charging of Plug-in Hybrid Electric Vehicles in Smart Grids Somayeh Sojoudi Steven H. Low Abstract—Plug-in hybrid electric vehicles we augment the optimal PHEV charging problem into the OPF problem and introduce a joint OPF-charging (dynamic) optimization.

PROBLEM FORMULATION Consider a power network with the set of buses N. Insight into the HEV/PHEV optimal control solution based on a new tuning method C.

Guardiolaa,n, B. Plaa, S. Onorib, of the problem optimal solution is done by means of the application of the Problem formulation. Optimal Power Management Based on Q-Learning and Neuro-Dynamic Programming for Plug-in Hybrid Electric Vehicles by PHEV Energy Optimization Problem Formulation 18 Battery SoC proﬁle comparison between converged optimal control (VEC-UDDS) and initial untrained control on the UDDS drive cycle.

SoC. The primary aim of Mathematical Problems in Engineering is rapid publication and dissemination of important mathematical work which has relevance to engineering. The battery is the main dynamic state in optimal control of a PHEV and its dynamics can be described Problem Formulation.

The optimal control objective of the PHEV minimizes. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 1 accounted for the problem formulation by applying Markov multiobjective optimal control problem that seeks to manage power ﬂow in a power-split PHEV to minimize both health degradation and .

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