Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles

Abstract

The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

A unified approach for capacity and power based sizing of electric machine and battery in P2 hybrid electric vehicles

“…However, numerical control strategies present various disadvantages: the strategy requires prior knowledge of the cycle, it can be only implemented offline, the problem is difficult to formulate, and its solution demands high computational overhead. 27,28 Analytical control strategies, such as the equivalent consumption minimization strategy (ECMS), are a suboptimal power management strategy capable of yielding results quite close to the optimal solution. 29,30 ECMS runs in real time and calculates the power share of the ICE and EM at each time instant.…”

“…However, numerical control strategies present various disadvantages: the strategy requires prior knowledge of the cycle, it can be only implemented offline, the problem is difficult to formulate, and its solution demands high computational overhead. 27,28…”

Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles

“…The offline methods include numerical techniques such as genetic algorithms [15] and dynamic programming [16][17][18][19][20], as well as analytical techniques such as Pontryagin's Minimum Principle [21,22]. Instead, when implementing online optimization techniques in FCHVs, the systems can be controlled through the Equivalent Consumption Minimization Strategy (ECMS) [23][24][25][26] and Model Predictive Control [27][28][29][30]. Although offline techniques are well-suited for defining the reference ideal solution, they cannot be applied in real time on a vehicle since they need previous information of the whole driving cycle.…”

“…They can be performed online, but need precise adjustment of the reference model and may incur high computational costs. The second category of techniques, referred to as rule-based methods, makes use of deterministic [31][32][33][34][35] or fuzzy logic [26,[36][37][38][39], and constitute the most well-known way of achieving real-time management in FCHV applications [40]. They are not model-based, and determine the operating point of the power source by means of rule tables to meet the requirements of other devices (e.g., battery or fuel cell) and the driver.…”