Lorenzo Serrao

A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles

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. DOI: 10.1115/1.4003267

ECMS as a realization of Pontryagin's minimum principle for HEV control

An analytical derivation of the Equivalent Consumption Minimization Strategy (ECMS) for energy management of hybrid electric vehicles (HEVs) is presented, based on Pontryagins 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.

An aging model of Ni-MH batteries for hybrid electric vehicles

The extensive use of batteries in hybrid electric vehicles (HEVs) today requires establishing an accurate model of battery aging and life. During a batterys lifetime, its performance slowly deteriorates because of the degradation of its electrochemical constituents. Battery manufacturers usually provide aging data that will show this degradation. However the data they provide result from standard aging tests, in which the battery is discharged and charged thousands of times with identical current profiles (or cycles). Using these data many aging models have been developed that relate the maximum number of battery cycles to the depth of discharge (DOD) of the current profile used. In this work, we focus on the development of an aging model suitable for applications in which the battery is used with no pre-defined cycles, as in the case of hybrid-electric vehicles. Laboratory experiments and concepts borrowed from fatigue analysis are applied to the relationship between battery aging and the most important operational conditions that affect its life, i.e. its operating temperature and current history.

Optimal control of power split for a hybrid electric refuse vehicle

An optimal power split strategy in a hybrid electric refuse truck is presented. Using Pontryagins Minimum Principle, a set of solution candidates is found and evaluated in order to find the optimal control strategy. Simulation results are shown to demonstrate the effectiveness of the strategy.

Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.Copyright

Layered control strategies for hybrid electric vehicles based on optimal control

Dynamic programming is known to provide the optimal solution to the energy management problem. However, it is not implementable online because it requires complete a-priori knowledge of the driving cycle and high computational requirements. This article presents a methodology to extract an implementable rule-based strategy from the dynamic programming results and thus build a near-optimal controller. The case study discussed in this paper focused on mode switching in a series/parallel hybrid vehicle, in which a clutch may be used to change the powertrain topology. Because of the complexity of the system, the controller is divided in two layers: the supervisory controller, which decides the powertrain configuration, and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described in detail. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by dynamic programming, which functions as a benchmark. Then another comparison is performed with respect to the equivalent consumption minimisation strategy (ECMS) which, if optimally tuned, can achieve optimal performance as close to DP as possible with the advantage of being implementable.

A RULE-BASED STRATEGY FOR A SERIES/PARALLEL HYBRID ELECTRIC VEHICLE: AN APPROACH BASED ON DYNAMIC PROGRAMMING

Dynamic programming (DP) provides the optimal global solution to the energy management problem for hybrid electric vehicles (HEVs), but needs complete a-priori knowledge of the driving cycle and has high computational requirements. This article presents a possible methodology to extract rules from the dynamic programming solution to design an implementable rulebased strategy. The case study considered is a series/parallel HEV, in which a clutch allows to switch from one configuration to another. The strategy works according to a two layer policy: the supervisory controller, which decides the powertrain configuration (either series or parallel), and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described. Then, the performance of the resulting rule-based strategy is studied and compared with thesolutiongivenbythedynamicprogramming, whichfunctions

A Novel Model-Based Algorithm for Battery Prognosis

Abstract The paper presents an analytical formulation of a damage accumulation law for automotive batteries, derived using curve fitting of experimental data from literature. The analytical formulation shows the equivalence of the proposed model to the Palmgren-Miner fatigue model used for mechanical components. The proposed model can be used to determine the residual life of automotive batteries and is potentially implementable on-line.

A two-step optimisation method for the preliminary design of a hybrid electric vehicle

In the present investigation an innovative procedure to design a hybrid electric vehicle (HEV) is proposed, based on two steps: optimisation and decision-making. Both steps require a multi-objective approach due to the many goals to be taken into account in the design of a complex system like an HEV. The method has been applied to the preliminary design of the powertrain and tuning of the control strategy of a series hybrid vehicle, simulated with a Matlab-Simulink code. The hardware parameters included the number of axles in the vehicle, number of electric motors per axle, and type and quantity of energy storage system devices (batteries and/or electrochemical capacitors). The control parameters are related to fuel economy conversion factors and the maximum and minimum state of charge allowed to the secondary energy storage systems. Several attributes of performance and fuel consumption evaluated with respect to seven driving cycles were considered as optimisation goals.

Model Predictive Control as an Energy Management Strategy for Hybrid Electric Vehicles

The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.Copyright