Battery consumption minimization strategy for plug-in, pure, and hybrid EVs
An energy management strategy for pure and hybrid electric vehicles that improves battery-life and optimizes fuel economy
Automotive powertrains are increasingly more electric, from mild and full hybrid, to plug-in hybrid and battery electric, automotive propulsion is increasingly relying on electricity as a source of power assist, or as the sole source of power, in automotive vehicles. This is true for passenger, commercial and off road vehicles. One of the most expensive subsystems in an electrified powertrain (by far the most expensive in a battery electric vehicle) is the energy storage system. Automakers pay significant attention in the design and operation phases to maximizing the life of the battery pack, so as not to incur undue warranty costs. But beyond warranty, it is the desire of all automakers to equip their vehicles with battery systems that will last as long as the expected life of the vehicle. The present invention consists of a control system that is capable of extending the life of a battery system by recognizing when it is being operated under conditions that could accelerate its aging. The invention is especially relevant to so-called charge sustaining hybrid vehicles, but is also relevant to plug-in hybrid electric vehicles.
The control system disclosed in the invention considers key factors that can accelerate aging in a battery system, aging being described by an increase in the internal resistance of the battery and/or a decrease of its energy storage capacity. Such key factors, which are also called “severity factors” include battery operating temperature, charge and discharge, or “C” rates, and battery state of charge. During operation of the vehicle, the control strategy automatically recognizes conditions that could result in accelerated aging, and reduces power output of the battery to minimize damage, by trading off fuel usage for battery life extension as dictated by severity conditions imposed by environmental and operating conditions. The novelty and importance of this invention is in the systematic optimization of this tradeoff to obtain the least reduction in fuel economy benefits in exchange for the greatest reduction in battery aging.
Embodiments of the invention use a methodology that accounts for battery aging in an energy management strategy for a hybrid electric vehicle. The background of the invention is that an optimal control problem is formulated to minimize fuel consumption as well as battery aging using battery cycle aging models derived from a mix of experiments and first-principle laws.
For proof of concept, the optimal control problem is solved using formal analytical and numerical approaches to solve optimal control problems, that is, Pontryagin’s Minimum Principle and Dynamic Programming. The cost function represents a (variable) tradeoff between normalized fuel economy and battery aging for various driving cycles. The results of these optimal control studies demonstrate that there indeed is a clear tradeoff between the two elements of the cost function, and that especially under harsh operating conditions it is possible to trade off a small decrease in fuel economy for substantial reduction in battery aging, as assessed by effective ampere-hour throughput, a quantity that takes a severity factor into consideration to quantify the aging effects of different on-vehicle operating conditions on the battery. The severity factor is a function of known operating conditions, such as battery temperature, state of charge, C-rate, and charging rate (for a plug-in hybrid), and is derived from the aging model.
While the formal methods used to understand the fundamental tradeoff between fuel economy and battery aging do not lend themselves to implementation in a production controller, the invention reported here has reduced these fundamental ideas to an implementation that is consistent with production controllers, leading to the concept of an aging-adaptive energy management strategy that optimizes the tradeoff between fuel economy and aging in response to actual vehicle use and environmental conditions.