Hybrid vehicle power management modeling and refinement

The power management strategy in many hybrid vehicles is based on expert rules and thresholds. These rule-based strategies can result in good efficiency in term of fuel economy and emissions if their thresholds and rules are accurate. However, due to the complexity and the non-linearity of the hybrid powertrain, determining accurate thresholds and rules is neither explicit nor straightforward, and experts in most cases fail to define these thresholds and rules with enough accuracy. Based on this fact, the objective of this paper is to propose a method to improve this rule-based strategy by refining its thresholds and rules. To achieve this, we used an optimization method (dynamic programming) to calculate the optimal power management, determine the optimal control signals, and extract efficient thresholds and rules that can be used in real time. Finally, simulation results for the three strategies (optimal, simple and refined strategy) are shown and discussed.     

Cooling effect of methanol on an n-heptane HCCI engine using a dual fuel system

Homogeneous charge compression ignition (HCCI) engines have the potential to raise the efficiency of reciprocating engines during partial load operation. However, the performance of the HCCI engine at high loads is restricted by severe knocking, which can be observed by the excessive pressure rise rate. This is due to the rapid combustion process occurring inside the cylinder, which does not follow the flame propagation that is seen in conventional engines. In this study, a low compression ratio of 9.5:1 for a gasoline engine was converted to operate in HCCI mode with the goal being to expand the stable operating region at high loads. Initially, pure n-heptane was used as the fuel at equivalence ratios of 0.30 to 0.58 with elevated intake charge temperatures of 180 and 90 °C, respectively. The n-heptane HCCI engine could reach a maximum performance at an indicated mean effective pressure (IMEP) of 0.38 MPa, which was larger than the performance found in the literature. To reach an even higher performance, a dual-fuel system was exploited. Methanol, as an anti-detonant additive, was introduced into the intake stream with various amounts of n-heptane at fixed equivalence ratios in the range of 0.42 to 0.52. It was found that the methanol addition cooled the mixture down prior to combustion and resulted in an increased coefficient of variation (COV). In order to maintain stable combustion and keep the pressure rise rate below the limit, the intake charge temperature should be increased. Introduction of 90% and 95% (vol/vol) hydrous methanol showed a similar trend but a lower thermal conversion efficiency and IMEP value. Therefore, a dual fuel HCCI engine could maintain a high thermal conversion efficiency across a wide load and enhance a 5% larger load compared to a pure n-heptane-fuelled HCCI engine. The hydrocarbon (HC) and carbon monoxide (CO) emissions were lower than 800 ppm and 0.10%, respectively. They were less at higher loads. The nitrogen oxides (NO _x ) emissions were below 12 ppm and were found to increase sharply at higher loads to a maximum of 23 ppm.     

Development of a fail-safe control strategy based on evaluation scenarios for an FCEV electronic brake system

This study presents a few fail-safe control strategies based on reliability evaluation scenarios for the electronic brake systems of green cars in several critical cases. CarSim and MATLAB Simulink were used to develop the FCEV model with regenerative braking involving EWBs and EMBs. The proposed reliability evaluation scenarios were simulated, and a few fail-safe control algorithms were verified using the proposed reliability evaluation scenarios with the developed FCEV simulation model. The reliability evaluation scenarios were developed using a combination of driving modes and FMEA results for these electronic brake systems.     

Nonlinear tire-road friction control based on tire model parameter identification

A hierarchical control structure is a more suitable structural scheme for integrated chassis control. Generally, this type of structure has two main functions. The upper layer manages global control and force allocation, while the bottom layer allocates realized forces with 4 independent local tire controllers. The way to properly allocate these target forces poses a difficult task for the bottom layer. There are two key problems that require attention: obtaining the nonlinear time-varying coefficient of friction between the tire and different road surfaces and accurately tracking the desired forces from the upper layer. This paper mainly focuses on longitudinal tire-road friction allocation and control strategies that are based on the antilock braking system (ABS). Although it is difficult to precisely measure longitudinal tire-road friction forces for frequently changing road surface conditions, they can be estimated with a real-time measurement of brake force and angular acceleration at the wheels. The Magic Formula model is proposed as the reference model, and its key parameters are identified online using a constrained hybrid genetic algorithm to describe the evolution of tire-road friction with respect to the wheel slip. The desired wheel slip, with respect to the reference tire-road friction force from the top layer, is estimated with the inverse quadratic interpolation method. The tire-road friction controller of the extended anti-lock braking system (Ext-ABS) is designed through use of the nonlinear sliding mode control method. Simulation results indicate that acceptable modifications to changes in road surface conditions and adequate stability can be expected from the proposed control strategy.     

Adaptive neural network based fuzzy control for a smart idle stop and go vehicle control system

Idle stop and go (ISG) is a low cost but very effective technology to improve fuel efficiency and reduce engine emissions by preventing unnecessary engine idling. In this study, a new method is developed to improve the performance of conventional ISG by monitoring traffic conditions. To estimate frontal traffic conditions, an ultra-sonic ranging sensor is employed. Several fuzzy logic algorithms are developed to determine whether the engine idling is on or off. The algorithms are evaluated experimentally using various data gathered in real areas with traffic congestion. The evaluation results show that the method developed can reduce the chance of false application of ISG significantly while improving fuel efficiency up to 15%.     

Development of a control strategy based on the transmission efficiency with mechanical loss for a dual mode power split-type hybrid electric vehicle

In this study, a control strategy for a dual mode power split-type hybrid electric vehicle (HEV) is developed based on the powertrain efficiency. To evaluate the transmission characteristics of the dual mode power split transmission (PST), a mechanical loss model of the transmission (TM loss) is constructed. The transmission efficiency, including the TM loss, is evaluated for the dual mode PST. Two control strategies for the dual mode PST are proposed. An optimal operation line (OOL) control strategy is developed to maintain a high engine thermal efficiency by controlling the engine operation point on the OOL. A speed ratio (SR) control strategy is proposed to obtain a greater transmission efficiency by shifting the engine operation point when the dual mode PST operates near the mechanical points. Using the TM loss and the proposed control strategies, a vehicle performance simulation is conducted to evaluate the performance of the two control strategies for dual mode PST. The simulation results demonstrate that, for the SR control strategy, the engine efficiency decreases because the engine operates beyond the OOL. However, the transmission efficiency of the dual mode PST increases because the PST operates near the mechanical point where the PST shows the greatest transmission efficiency. Consequently, the fuel economy of the SR control strategy is improved by 3.8% compared with the OOL control strategy.     

Robust design optimization of suspension system by using target cascading method

This study presents the robust design optimization process of suspension system for improving vehicle dynamic performance (ride comfort, handling stability). The proposed design method is so called target cascading method where the design target of the system is cascaded from a vehicle level to a suspension system level. To formalize the proposed method in the view of design process, the design problem structure of suspension system is defined as a (hierarchical) multilevel design optimization, and the design problem for each level is solved using the robust design optimization technique based on a meta-model. Then, In order to verify the proposed design concept, it designed suspension system. For the vehicle level, 44 random variables with 3% of coefficient of variance (COV) were selected and the proposed design process solved the problem by using only 88 exact analyses that included 49 analyses for the initial meta-model and 39 analyses for SAO. For the suspension level, 54 random variables with 10% of COV were selected and the optimal designs solved the problem by using only 168 exact analyses for the front suspension system. Furthermore, 73 random variables with 10% of COV were selected and optimal designs solved the problem by using only 252 exact analyses for the rear suspension system. In order to compare the vehicle dynamic performance between the optimal design model and the initial design model, the ride comfort and the handling stability was analyzed and found to be improved by 16% and by 37%, respectively. This result proves that the suggested design method of suspension system is effective and systematic.     

Possible experimental method to determine the suspension parameters in a simplified model of a passenger car

Currently, as well as in the past, researchers have shown great interest in developing suspension systems for vehicles and especially in the design and optimization of the suspension parameters, such as the stiffness and the damping coefficient. These parameters are considered to be important factors that have an influence on safety and improve the comfort of the passengers in the vehicle. This paper describes a simplified methodology to determine, in a quick manner, the suspension parameters for different types of passenger cars equipped with passive suspension systems. Currently, different types of passenger cars are produced with different types of suspension systems. Finding a simplified methodology to determine these parameters with sufficient accuracy would contribute a simplified and quick method to the inspection of the working conditions of a suspension system. Therefore, a simple system to determine these parameters is needed. An analysis of the suspension parameters is performed using mathematical modeling and numerical analysis conducted using the Working Model software. The result derived from the developed methodology shows small errors when compared with the generic values, and it can be concluded that the design of the suspension parameter measurement device using the developed methodology is useful, simple, and has sufficient accuracy.     

SCR control strategy based on ANNs and Fuzzy PID in a heavy-duty diesel engine

The paper presents an innovative method combining artificial neural networks (ANNs) with Fuzzy PID to demonstrate the advantages of this control approach for meeting both NOx emission requirements and NH3 slip targets. An ANN model was utilized to simulate the formation of NOx emissions under various engine operating conditions. Next, an effective closed-loop control strategy with a type of feedback known as fuzzy PID is adopted for on-line, real-time control of 32.5% aqueous urea dosing in the exhaust stream. The new strategy explores the benefits by simulation and testing in the environments of Matlab/Simulink and ESC/ETC, respectively. The notable achievement of considerable NOx reduction and an acceptably small NH3 slip is obtained based on this new, feasible and effective strategy.     

Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition

By considering the effect of the driving cycle on the energy management strategy (EMS), a fuzzy EMS based on driving cycle recognition is proposed to improve the fuel economy of a parallel hybrid electric vehicle. The EMS is composed of driving cycle recognition and a fuzzy torque distribution controller. The current driving cycle is recognized by learning vector quantization in driving cycle recognition. The torque of the engine and the motor is controlled by a fuzzy torque distribution controller based on the required torque of the hybrid powertrain and the battery state of charge. The membership functions and rules of the fuzzy torque distribution controller are optimized simultaneously by using particle swarm optimization. Based on the identification results of driving cycle recognition, the fuzzy torque distribution controller selects the corresponding membership function and rule to control the hybrid powertrain. The simulation research based on ADVISOR demonstrates that this EMS improves fuel economy more effectively than fuzzy EMS without driving cycle recognition.