Electric Assist Control Strategy Research Articles

Energy Management of Parallel Hybrid Electric Vehicle Based on Fuzzy Logic Control Strategies

Currently, the parallel hybrid electric vehicle (PHEV) is the most common type of architecture on the hybrid vehicle market. Therefore, a PHEV can be a solution to reduce emission and fuel consumption. The main challenge in the development of HEVs is the power management between the components that ensure vehicle movement. Energy management is now highly necessary by applying a control strategy (CS) in the vehicle’s traction chain, which directly affects the PHEV emission and fuel economy. The CSs have different performances, namely the control of the different power sources operation mode and the control of the battery state of charge. For this purpose, we propose a fuzzy logic CS to optimize emissions (FLCS-em) for PHEV. To assess this approach, we compare it with the most commonly used and recent EMS, in particular the strategy to optimize fuel use (FLCS-f), the efficiency optimization strategy (FLCS-eff) and the electric assist CS (EACS), in urban and highway driving cycles. The results show that the elaborate FLCS-em, characterized by a limited number of rulers, provide significant advantage than CSs mentioned in terms of the efficiency of PHEV performance and emissions and fuel consumption minimization.

Research on Key Technologies of Computer Simulation for Vehicle Engineering Engine

The paper analyses the importance of simulation technology for the research and development of hybrid engines in vehicle engineering, and introduces the current development status and key technologies of hybrid engine simulation technology, and puts forward the focus of future research. Simultaneously, taking the hybrid electric vehicle as a case, through the analysis of the drive structure and work requirements, to optimize the power performance and fuel economy of the vehicle, the electric assist control strategy for the hybrid power system is proposed. The control logic formulates the driving conditions. A related control model was established, and the control strategy was simulated on the Advisor software platform. The simulation results show that the proposed electric-assisted control strategy is entirely suitable for hybrid electric vehicles. Compared with traditional vehicles, the power performance and fuel economy of the whole vehicle are further improved.

A New Framework for Advancement of Power Management Strategies in Hybrid Electric Vehicles

Power management strategies play a key role in the design process of hybrid electric vehicles. Electric Assist Control Strategy (EACS) is one of the popular power management strategies for hybrid electric vehicles (HEVs). The present investigation proposes a new framework to advance the EACS. Dynamic Programming method is applied to an HEV model in several drive cycles, and as a result, some optimal operating regions are found. The obtained regions are almost distinct, and consequently, some threshold lines can be defined to separate them. The obtained threshold lines are used to eliminate some parameters of the EACS to reduce its sensitivity to the driving behavior. It is shown that by applying the mentioned modification, the sensitivity of the EACS decreases without a significant increase in the HEV’s FC. All in all, our findings indicate the effectiveness of the proposed methodology to improve the EACS strategy for HEV supervisory control applications.

Torque-Leveling Threshold-Changing Rule-Based Control for Parallel Hybrid Electric Vehicles

A novel rule-based control strategy is proposed for the energy management of parallel hybrid electric vehicles (HEVs): the torque-leveling threshold-changing strategy (TTS). In contrast to the most commonly used heuristic electric assist control strategy (EACS) that is designed based on the load following approach, the TTS proposes and applies the new fundamental concept of torque leveling. This mechanism operates the engine with a constant torque when the engine is active, thus ensuring the engine works at an efficient operating point. The TTS additionally extends and uses a design concept that has previously been proposed in the context of series HEVs, the threshold-changing mechanism, to operate the HEV in a charge-sustaining manner. By exploiting this new set of design principles for parallel HEVs, the TTS realizes energy source control sharing behavior that is reminiscent to optimization-based methods. To show its effectiveness, the TTS is implemented to a through-the-road (TTR) HEV and benchmarked against two conventional control strategies: dynamic programming (DP) and the EACS. The results show that the TTS, despite its simplicity, is able to deliver comparable fuel economy as the global optimization approach DP and thus achieve significant improvement compared to the EACS. In addition, to facilitate real-time application, a simplified version of the TTS (STTS) is also developed, which is able to deliver similar performance as the TTS but is more simple to implement in practice.

An Optimal Control Strategy for Parallel Hybrid Electric Vehicles

This paper proposes a new power management strategy (PMS) for parallel hybrid electric vehicles equipped with continuously variable transmission (CVT). The proposed PMS is established on the basis of electric assist control strategy (EACS) and equivalent consumption minimization strategy (ECMS). This control approach is based on maintaining the battery energy within a recommended range, considering the CVT efficiency in selecting the engine operating point, and finding the best power split between the engine and electric motor at certain moments of the driving. In order to evaluate the effectiveness of this scheme, it is compared with EACS, a modified version of EACS and ECMS. It is shown that, in all of the studied driving cycles, the proposed PMS is superior to the considered rival strategies in terms of the fuel consumption and also HC and CO emissions.

Optimal energy management strategy of a hybrid electric vehicle considering engine noise

Noise emission from vehicles in urban transportation has become of interest for researchers in addition to the engine exhaust gas emissions due to its significant effect on public health. In this work, an optimal energy management strategy is proposed for a hybrid electric vehicle (HEV) by taking the effect of engine noise into account. The engine noise is calculated based on a pressure-based combustion noise model at different operating points of a 1.5 L gasoline engine. The optimal operating points of the engine are defined using the calculated engine noise from in-cylinder pressure data and experimental data of brake specific fuel consumption (FC). A modification on the electric assist control strategy is proposed to mask the engine noise below the road noise. The modified strategy is then optimized for different driving cycles. Comparison of the results demonstrates that the proposed modification not only masks the engine noise below the road noise, but also reduces the vehicle FC.

NSGA-II multi-objectives optimization algorithm for energy management control of hybrid electric vehicle

The currently existing energy management control optimization for hybrid electric vehicle (HEV) mainly focuses on fuel economy. Apart from this, there has been some consideration of the impact of emissions, but almost no attention has been paid to drivability performance. Therefore, from the point of view of multi-objectives optimization, the influences of fuel economy, emission and drivability performance on the energy management are comprehensively considered for a parallel HEV. The energy management control parameters and driveline parameters are selected to be optimized parameters. Then, the NSGA-II (Fast Non-dominated Sorting Genetic Algorithm-II) algorithm is proposed to solve the multi-objectives optimization problem. Furthermore, the multi-objectives optimization method for HEV energy management control is established and comparatively simulated with the parallel electric assist control strategy. The results show that the evaluation index of drivability decreases by 27.12% from the maximum and the average enhancement effect of optimization falls by 20.84%. The evaluation index of fuel economy declines by 22.30% from the maximum and the average index drops by 20.26%. The comprehensive index of emission performance descends by 11.33% from the maximum. The proposed multi-objectives optimization algorithm has good convergence and distribution, and obtains more Pareto optimal solution sets, which can provide more selectivity in building HEV energy management control strategies.

Investigating the effect of engine noise on power management strategy of a hybrid electric vehicle

Hybrid electric vehicles (HEVs) have been developed as a promising way to decrease the fuel consumption and emissions of conventional vehicles. Although the noise emission of HEVs is generally lower than that of conventional vehicles, it is still an issue, especially in urban transportation. In this paper, a power management strategy is developed to minimize the annoying noise of the engine for an HEV. This is a modified version of the strategy that was originally established based on the speed ratio of continuously variable transmission (CVT) as the control parameter (CVT-based strategy). The engine combustion noise is assessed using the experimental data of the in-cylinder pressure. Also, the engine brake specific fuel consumption (bsfc) is defined from the experiments. The bsfc and noise data are implemented in the power management strategy. The proposed strategy offers a better performance in terms of reducing engine noise and fuel consumption in comparison with an electric assist control strategy (EACS). On the other hand, the proposed strategy results in a lower level of engine noise than the original CVT-based strategy at the expense of slightly increasing the fuel consumption. For instance, the noise level (dB) in an urban dynamometer driving cycle using the proposed strategy is 49% lower than the case of CVT-based strategy, while the vehicle FC is about 1.1% more than the CVT-based case.

Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm

Hybrid electric vehicle (HEV) control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a “memetic algorithm (MA)” to optimize the control parameters in parallel HEVs. The “local search” mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR) and is set up according to an electric assist control strategy (EACS) to minimize the fuel consumption (FC) and emissions (HC, CO, and NOx) of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC), Federal Test Procedure (FTP), Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC), and urban dynamometer driving schedule (UDDS) are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.

Genetic algorithm with small population size for search feasible control parameters for parallel hybrid electric vehicles

The control strategy is a major unit in hybrid electric vehicles (HEVs). In order to provide suitable control parameters for reducing fuel consumptions and engine emissions while maintaining vehicle performance requirements, the genetic algorithm (GA) with small population size is applied to search for feasible control parameters in parallel HEVs. The electric assist control strategy (EACS) is used as the fundamental control strategy of parallel HEVs. The dynamic performance requirements stipulated in the Partnership for a New Generation of Vehicles (PNGV) is considered to maintain the vehicle performance. The known ADvanced VehIcle SimulatOR (ADVISOR) is used to simulate a specific parallel HEV with urban dynamometer driving schedule (UDDS). Five population sets with size 5, 10, 15, 20, and 25 are used in the GA. The experimental results show that the GA with population size of 25 is the best for selecting feasible control parameters in parallel HEVs.

Multi-objective Optimization of Hybrid Electric Vehicle Equipped with Power-split Continuously Variable Transmission

A B S T R A C T This paper aims to find the efficient state of hybrid electric vehicle (HEV) by simultaneous optimization of the control strategy and the power train. The power transmission employed in this vehicle is a power-split continuously variable transmission (CVT) which uses several fixed ratio mechanisms. After describing this transmission, the rules of electric assist control strategy are introduced. A modification on this strategy is proposed to decrease the start/stop frequency of the engine and electric motor. Afterwards, an optimization on the HEV’s power train size, power-split CVT and the control strategy is accomplished with the aim of minimizing the vehicle fuel consumption and emissions. The optimization results are some Pareto-optimal points which are tradeoff solutions between fuel consumption and emissions. Finally, in order to intuitively demonstrate the optimality of the optimization results, the Pareto points for the case of considering two objectives are shown.

A new control strategy for hybrid electric vehicles equipped with a continuously variable transmission

The electric assist control strategy is one of the well-known methods for managing the power sources of hybrid electric vehicles. Because of the dependence of the electric assist control strategy approach on its parameters, optimization of this strategy can improve its performance, which results in lower fuel consumption and lower emission levels. However, one of the main concerns about optimization of this strategy is its dependence on the driving behaviour. This paper aims to propose a new control strategy based on the electric assist control strategy with a smaller number of control parameters and less dependence on the driving behaviour. This strategy is usable only for vehicles equipped with a continuously variable transmission. In this strategy, the engine’s operating point is determined by considering its best point in terms of the fuel consumption and the emissions, and also the high-efficiency region of the powertrain. After optimization of the proposed strategy and the electric assist control strategy approach, the methods are compared. It is shown that the proposed strategy provides a better performance in terms of the fuel consumption and the emissions during all the considered driving cycles. Moreover, it is demonstrated that the performance of the proposed strategy has less dependence on the driving-cycle pattern because fewer parameters are used than in the electric assist control strategy. This feature makes the devised control strategy desirable for real-world conditions where the vehicle undergoes various driving patterns.

Application of a Pheromone-Based Bees Algorithm for Simultaneous Optimisation of Key Component Sizes and Control Strategy for Hybrid Electric Vehicles

A Pheromone-Based Bees Algorithm (PBA) is employed to optimize the key component sizes and control strategy for parallel Hybrid Electric Vehicles (parallel HEVs) presented. The Basic Bees Algorithm (BBA) is an intelligent optimization tool mimicking the food foraging behavior of honey bees. In this research, however, a new version of BBA which uses pheromones, chemical substances secreted by bees and other insects into their environment, enabling them to communicate with other members of their own species, is applied. The PBA employs the pheromone to attract bees to explore the promising regions of the search space, and the parallel HEV configuration and an Electric Assist Control Strategy are used to formulate the research. The value of the key component size and control strategy parameters is adjusted according to PBA to obtain the minimization of weighted sum of Fuel Consumption (FC) and emissions while vehicle performance that satisfy the PNGV constraints. In this research, ADVISOR software has been used as the simulation tool, and driving cycles, FTP, ECE-EUDC and UDDS, are employed to evaluate FC, emissions and dynamic performances. Following a description of the algorithm, the paper shows the results obtained for the simultaneous optimization of key component sizes and control strategy for parallel Hybrid Electric Vehicles. The results prove that PBA is a strong algorithm for determining the optimal parameters of component sizes and control strategy resulting in improvement of FC and emissions without sacrificing vehicle performance. Compared to BBA, the new version, PBA, showed an improvement of about 25% in convergence speed with the nearly same results of optimization targets.

Control strategy parameter analysis of parallel hybrid electric vehicles based on multiindex orthogonal experiment

Concerning the parameter optimization of electric assist control strategy for Hybrid Electrical Vehicles(HEV),the orthogonal table was designed based on the theory of orthoplan experiment,which took the fuel consumption,the CO's emission,and the total emission of HC and NOx as the experimental indexes.The influence of control strategy parameters on the parallel HEV was obtained by using intuitive analysis to analyze the 18 group test results,and then the remarkably influencing factors of each index were found out.

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles

This paper presents the optimization of key component sizes and control strategy for parallel hybrid electric vehicles (parallel HEVs) using the bees algorithm (BA). The BA is an intelligent optimization tool that mimics the food foraging behavior of honey bees. Parallel HEV configuration and electric assist control strategy were used to conduct the research. The values of the key component size and the control strategy parameters were adjusted according to the BA to minimize the weighted sum of fuel consumption (FC) and emissions, while the vehicle performance satisfies the PNGV constraints. In this research, the software ADVISOR was used as the simulation tool, and the driving cycles FTP, ECE-EUDC and UDDS were employed to evaluate FC, emission and dynamic performance. The results demonstrate that the BA is a powerful tool in parallel HEV optimization to determine the optimal parameters of component sizes and control strategy, resulting in the improvement of FC and emissions without sacrificing vehicle performance. In addition, the BA is able to define a global solution with a high rate of convergence.

Control Strategy for a 42-V Waste-Heat Thermoelectric Vehicle

A 42-V waste-heat thermoelectric vehicle is employed as a potential application of thermoelectric generators for fuel economy improvement and emissions reduction. The 42-V waste-heat thermoelectric vehicle currently in development employs an assemblage driving system consisting of a waste-heat thermoelectric generator, a 42-V powernet, and an integrated starter and generator (ISG). The waste-heat thermoelectric generator also functions as a power supply. To optimize the utilization of the waste-heat energy generated by the thermoelectric generator, an electric assist control strategy and a torque split control strategy are proposed herein. Through the development of relevant systems and strategies, including the thermoelectric generator and an electric bus system, two vehicle models are established and compared using the ADVISOR platform based on MATLAB/Simulink. The calculation results show improved fuel economy and emissions performance resulting from the integration of the torque split control strategy into the 42-V waste-heat thermoelectric vehicle.

A Research on the Fuzzy Control Strategy for Parallel Hybrid Electric Vehicle

Several kinds of methods of energy management of hybrid electric vehicle (HEV) are analyzed. Based on the design requirement of a certain type of parallel HEV, the fuzzy control strategy of energy management is proposed. ADVISOR2002 is chosen as the simulation platform for secondary development, and the simulation results of the fuzzy control strategy and electric assist control strategy are compared. The simulation results indicate that the adaptive fuzzy controller can obviously improve the performance of HEV fuel economy and emissions.

Multiobjective Optimization of HEV Fuel Economy and Emissions Using the Self-Adaptive Differential Evolution Algorithm

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE+EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design.

Optimal gain scheduling control strategy for parallel HEV based on traffic conditions

This paper presents gain scheduling of control strategy for parallel hybrid electric vehicles, based on traffic conditions. Electric assist control strategy (EACS) is employed, with different parameters for different traffic conditions. The parameters of the EACS are optimised and scheduled for different traffic conditions of the TEH-CAR driving cycle. TEH-CAR is a driving cycle which is developed, based on the experimental data collected from the real traffic conditions in the city of Tehran. The objective of the optimisation is to minimise the fuel consumption and emissions over the driving cycle, while enhancing or maintaining the driving performance characteristics of the vehicle. A genetic algorithm (GA) is used to solve the optimisation problem, and the constraints are handled by using penalty functions. The results from the computer simulation show the effectiveness of the approach and reduction in fuel consumption and emissions, while ensuring that the vehicle performance is not sacrificed.