Simona Onori

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.

Lithium-ion batteries life estimation for plug-in hybrid electric vehicles

This paper deals with life estimation of lithium batteries for plug-in hybrid electric vehicles (PHEVs). An aging model, based on the concept of accumulated charge throughput, has been developed to estimate battery life under “real world” driving cycles (custom driving cycles based on driving statistics). The objective is to determine the “damage” on the life related to each driving pattern to determine equivalent miles/years. Results indicates that Lithium-ion batteries appear to be 10 year/150,000 mile capable, provided that they are not overcharged, nor consistently operated at high temperatures, nor in charge sustaining mode at a very low state of charge.

Optimal energy management of hybrid electric vehicles including battery aging

The paper presents a methodology to account for battery aging in the energy management strategy for a hybrid electric vehicle. An optimal control problem is formulated to minimize fuel consumption as well as battery aging, using recently developed methods for battery lifetime modeling. The approach relies on the concept of severity factor map, a tool used to quantify the aging effects of a battery due to its different on-vehicle operating conditions. The optimal control problem is solved using Pontryagins Minimum Principle, showing with simulations the effect of the new control approach compared to the standard energy management strategies.

A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications

This paper presents a new approach to life estimation for lithium-ion batteries used in plug-in hybrid electric vehicles (PHEVs) applications. A new framework for battery life estimation is developed which investigates the effects of two primary factors of battery life reduction in PHEVs applications, namely, depth of discharge (DOD) and temperature (Tbatt), under typical driving conditions, driving habits, and average commute time of typical user over a year. This framework, whose development is built upon a weighted ampere-hour throughput model of the battery, is based on the novel concept of severity factor map which captures and quantifies the battery damage caused by different operating conditions. The proposed methodology can be a suitable tool to estimate battery life in terms of miles/year on-board of the vehicle.

A magnitude and rate saturation model and its use in the solution of a static anti-windup problem

In this paper we address the anti-windup design problem for linear control systems with strictly proper controllers in the presence of input magnitude and rate saturation. Using generalized sector condition, we provide an LMI-based procedure for the construction of a linear anti-windup gain acting on the controller state equation such that regional closed-loop stability is guaranteed and suitable performance measures are optimized. The approach is successfully illustrated on a simulation example.

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

Energy Management Strategy for HEVs Including Battery Life Optimization

This paper presents an optimal control-based energy management strategy for a parallel hybrid electric vehicle (HEV). Not only does this strategy try to minimize fuel consumption while maintaining the state of charge of the battery within reasonable bounds, it also seeks to minimize wear of the battery and extend its life. This paper focuses on understanding the optimal control solution offered by Pontryagins minimum principle (PMP) in this context. Simulation-based results are presented and analyzed, which show that the control algorithm is able to reduce battery wear by decreasing battery operating severity factor with minimal fuel economy penalty. The benefit of this strategy is especially evident when ambient and driving conditions are especially severe.

Model-based life estimation of Li-ion batteries in PHEVs using large scale vehicle simulations: An introductory study

Plug-In Hybrid Electric Vehicles (PHEVs) are a promising mid-term solution to reduce the energy demand in the personal transportation sector, due to their ability of storing energy in the battery through direct connection to the electrical grid. However, an important aspect to a successful market acceptability for these vehicles is related to the reliability of the energy storage system.

Cloud-computing based velocity profile generation for minimum fuel consumption: A dynamic programming based solution

This paper proposes a new framework to minimize the fuel consumed in a conventional vehicle over a given driving route by finding the optimal velocity profile. The optimization problem is solved in a remote cloud computing environment and assumes the vehicle route to be known a priori. A spatial domain dynamic programming optimization algorithm is used in this study to find the optimal velocity profile. The cloud-computing environment integrates information from GIS, road speed limits, into a vehicle simulator equipped with a fuel consumption model to predict fuel use along the desired route. The resulting global optimal velocity profile is sent back to the driver for velocity advisory.