Fuzzy Gain-Scheduling Proportional–Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle

Syed, Fazal; Kuang, Ming; Smith, Matt; Okubo, Shuhei; Ying, Hao

doi:10.1109/tvt.2008.923690

Cited by 87 publications

(47 citation statements)

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“…V oc (t) and R b (t)) are polynomial functions of the battery SOC. The electrical power generated by the battery is (6) and accordingly, the consumed electrical energy of the battery is…”

Section: Electrical Pathmentioning

confidence: 99%

“…The energy management strategies, also called supervisory control strategies, can be grouped into three categories: rulebased control strategies [5,6,7], optimization-based control strategies [8,9,10], and real-time control strategies [11,12,13]. In the rule-based control strategies, the rules are designed using heuristics, human expertise, or mathematical models.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

Zhao¹,

Stobart²

2013

SAE Int. J. Alt. Power.

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INTRODUCTIONThe White House announced new fuel efficiency standards for trucks, buses, and other heavy duty vehicles in August 2011. Within the standards, vehicles manufactured between 2014 and 2018 are required to reduce their fuel consumption and greenhouse gas emissions by 10% to 20%. HEVs are considered as the most promising short-term solutions in reducing the fuel consumption [1,2]. By exploiting the capacity of a storage system installed aboard, HEVs can achieve better fuel economy and lower exhaust emissions than traditional powertrain vehicles. In a HEV, an electrical path is added to the powertrain such that part of the vehicle kinetic energy and exhaust gas energy can be captured by the electrical machines, and be used to recharge the battery. The energy assisted by the electrical machines also helps downsizing the internal combustion engine (ICE), resulting in better fuel efficiency and lower heat loss [3]. Since electrical machines provide faster boosting torque than mechanical systems, HEVs offer the improved launching performance and the reduced overall rated power comparing with traditional ICE vehicles.A key issue in developing HEVs is the coordination of multiple energy flow of both fuel path and electrical path, that is the management of the power distribution in parallel paths to minimize the fuel consumption, while satisfying the constraints of power demand and battery SOC [4]. The energy management strategies, also called supervisory control strategies, can be grouped into three categories: rulebased control strategies [5,6,7], optimization-based control strategies [8,9,10], and real-time control strategies [11,12,13]. In the rule-based control strategies, the rules are designed using heuristics, human expertise, or mathematical models. Although the control approaches can offer improvement on energy efficiency, it is clear that they do not guarantee an optimal result in all conditions. Using the optimization-based control strategies, the global optimum solution can be found by performing the optimization over a fixed drive cycle. Unfortunately, the global optimization approach is noncasual in nature, because the prior knowledge of the future driving information is required. The real-time optimization strategies manage the energy flow online, where the most well-known approach is the ECMS. The ECMS realizes the real-time optimization by minimizing an instantaneous cost function, so behaves as a closed-loop controller. The equivalent fuel consumption is defined as the extra energy which is offered by the battery and is effected by both the engine operating condition and the supervisory control action. It is assumed that the variation of battery SOC will be compensated in the Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric VehiclesDezong Zhao and Richard StobartLoughborough University ABSTRACTThe performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for impro...

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“…V oc (t) and R b (t)) are polynomial functions of the battery SOC. The electrical power generated by the battery is (6) and accordingly, the consumed electrical energy of the battery is…”

Section: Electrical Pathmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

Zhao¹,

Stobart²

2013

SAE Int. J. Alt. Power.

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“…They concluded that, with ECMS, frequent shifting should be avoided by adding extra constraints between gear switching decisions, while with stochastic dynamic programming approach (SDP) an extra input operating gear mode is needed beside the engine speed and the SOC. Syed et al [8] used a fuzzy logic gain scheduling algorithm with proportional-integral (PI) controllers which are used with power-split HEV (PSHEV). The results of testing the controller on a Ford Escape showed that a minimum of four rules are needed to ensure smooth engine speed.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management Control for Hybrid Electric Powertrains

Zaher

Cetinkunt

2013

Journal of Control Science and Engineering

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This paper focuses on embedded control of a hybrid powertrain concepts for mobile vehicle applications. Optimal robust control approach is used to develop a real-time energy management strategy. The main idea is to store the normally wasted mechanical regenerative energy in energy storage devices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is in the opposite direction to the applied force or torque. This is the case when the vehicle is braking, decelerating, the motion is driven by gravitational force, or load driven. There are three main concepts for energy storing devices in hybrid vehicles: electric, hydraulic, and mechanical (flywheel). The real-time control challenge is to balance the system power demands from the engine and the hybrid storage device, without depleting the energy storage device or stalling the engine in any work cycle. In the worst-case scenario, only the engine is used and the hybrid system is completely disabled. A rule-based control algorithm is developed and is tuned for different work cycles and could be linked to a gain scheduling algorithm. A gain scheduling algorithm identifies the cycle being performed by the work machine and its position via GPS and maps both of them to the gains.

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“…The rule-based control strategies can be easily implemented in real-world vehicles, such as the bang-bang control [16], [17], fuzzy logic [18]- [20], and neural networks [21], [22]. The rules are designed using heuristics, human expertise, or mathematical models.…”

mentioning

confidence: 99%

“…Although the control approaches can offer improvement in energy efficiency, it is clear that they do not guarantee an optimal result in all conditions. As a brief discussion of fuzzy logic control strategies in HEV, [18] used the fuzzy logic to tune the PI controller to achieve a smoother performance, but did not consider the rapid changing road dynamics; [19] focused on battery SOC management but took neither emissions nor engine efficiency into account; [20] is capable to maintain the battery SOC in a reasonable range, rather than achieving the demand value at the end of the test cycle.…”

mentioning

confidence: 99%