Hybrid electric vehicle (HEV) improvements in fuel economy and emissions strongly depend on the energy management strategy. The control of an HEV with minimum fuel consumption and emissions is a global problem and the control action taken at each time instant affects the following. Thus, dynamic programming (DP) is a well-suited technique to find the optimal solution to the control problem. Unfortunately, this approach to solving the optimal control problem requires a priori knowledge of the driving conditions (necessary to implement the DP backward algorithm) and is therefore not suitable for HEV real-time control. It is shown that it is possible to obtain the global optimal control policy using the instantaneous minimization of a “well-defined” cost function dependent only on the system variables at the current time. The definition of such a cost function requires an equivalence factor for comparing the electrical energy with the fuel energy. This approach is known in literature as equivalent consumption minimization strategy (ECMS). The optimal value of the equivalence factor can be found through a systematic optimization only if the driving cycle is known. In this paper a new control strategy called adaptive ECMS (A-ECMS) is presented. This real-time energy management for HEV is obtained adding to the ECMS framework an on-the-fly algorithm for the estimation of the equivalence factor according to the driving conditions. The main idea is to periodically refresh the control parameter according to the current road load, so that the battery state of charge is maintained within the boundaries and the fuel consumption is minimized. The results obtained with A-ECMS show that the fuel economy that can be achieved is only slightly suboptimal and the operations are charge-sustaining.

Hybrid electric vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. The parallel HEV control problem involves the determination of the time profiles of the power flows from the engine and the electric motor. This is also referred to as the power split between the conventional and the electric sources. The objective of HEV control is in fact to find out the sequence of optimal power splits at each instant of time that minimizes the fuel consumption over a given driving schedule. Big obstacles to the control design are the model complexity and the necessity of "a priori" knowledge of torque and velocity profiles. This paper presents three different energy management approaches for the control of a parallel hybrid electric sport-utility-vehicle that do not require a priori knowledge of the driving cycle. The considered approaches are: a rule-based control, an adaptive equivalent fuel consumption minimization strategy (A-ECMS), and the H_infin control. Results, compared with the optimal solution given by the dynamic programming, show that the A-ECMS strategy is the best performing strategy

This paper presents a formalization of the energy management problem in hybrid electric vehicles and a comparison of three known methods for solving the resulting optimization problem. Dynamic programming (DP), Pontryagin’s minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) are described and analyzed, showing formally their substantial equivalence. Simulation results are also provided to demonstrate the application of the strategies. The theoretical background for each strategy is described in detail using the same formal framework. Of the three strategies, ECMS is the only implementable in real time; the equivalence with PMP and DP justifies its use as an optimal strategy and allows to tune it more effectively.

Demand response programs are currently being proposed as a solution to deal with issues related to peak demand and to improve the operation of the electric power system. In the demand response paradigm, electric utilities provide incentives and benefits to private consumers as a compensation for their flexibility in the timing of their electricity consumption. In this paper, a dynamic energy management framework, based on highly resolved energy consumption models, is used to simulate automated residential demand response. The models estimate the residential demand using a novel bottom-up approach that quantifies consumer energy use behavior, thus providing an accurate estimation of the actual amount of controllable resources. The optimal schedule of all of the controllable appliances, including plug-in electric vehicles, is found by minimizing consumer electricity-related expenditures. Recently, time-varying electricity rate plans have been proposed by electric utilities as an incentive to their customers with the objective of re-shaping the aggregate demand. Large-scale simulations are performed to analyze and quantitatively assess the impact of demand response programs using different electricity price structures. Results show that simple time-varying electricity price structures, coupled with large-scale adoption of automated energy management systems, might create pronounced rebound peaks in the aggregate residential demand. To cope with the rebound peaks created by the synchronization of the individual residential demands, innovative electricity price structures-called Multi-TOU and Multi-CPP-are proposed.

An analytical derivation of the equivalent consumption minimization strategy (ECMS) for energy management of hybrid electric vehicles (HEVs) is presented, based on Pontryagin's minimum principle. The derivation is obtained using a generic formulation of the energy management problem in HEVs and is valid for any powertrain architecture. Simulation results obtained for a series HEV are also provided.