Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Real-time predictive energy management of plug-in hybrid electric vehicles for coordination of fuel economy and battery degradation

Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

https://www.mdpi.com/1996-1073/13/13/3352/pdf

Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook

As an efficient control strategy, model predictive current control (MPCC) has rapid response and simple calculation. This article proposes an improved MPCC scheme for permanent-magnet synchronous hub motor (PMSHM) drives. The mentioned control scheme uses the parameter values at the last moment to obtain the back electromotive force (EMF) and utilizes the obtained back EMF to obtain the predicted current value at the next moment. In the actual application of the motor, to enhance the robustness of the control system, a sliding mode controller is used to replace the conventional proportional-integral (PI) speed loop, and a finite position phase-locked loop based on the dichotomy is added to achieve sensorless speed control and provide an accurate rotor position angle. To improve the steady-state performance, the method of duty cycle is introduced, and the null vector and the actual vector are used together in the same control cycle. The simulation and experimental results both show the effectiveness of the proposed MPCC scheme, and the steady-state performance of MPCC is greatly improved compared with traditional MPCC.

Speed Sensorless Model Predictive Current Control Based on Finite Position Set for PMSHM Drives

This article analyzes various configurations of Hybrid Energy Storage Systems consisting of batteries only, combinations of batteries and supercapacitors, and supercapacitors only. For the presented configurations, mathematical models that were used in research in terms of energy consumption and carbon dioxide emissions were developed, employing a 12-m city bus as a test bed. The tests were carried out using standard test cycles for heavy vehicles as well as routes developed on the basis of actual road conditions. The obtained test results confirmed that the lowest energy consumption is characterized by the system supplied exclusively by batteries (855 Wh/km), followed by a hybrid system of a large battery with a small supercapacitor (941 Wh/km), a hybrid system with a large supercapacitor and a small battery pack (1087 Wh/km), and finally a system with a supercapacitor only (1091 Wh/km). In comparison with the conventional diesel power system (3967 Wh/km), the CO2 emission reductions ranged from 27% to 43%, depending on the source of electrical energy.

Studies of Energy Consumption by a City Bus Powered by a Hybrid Energy Storage System in Variable Road Conditions

The fuel economy of hybrid electric vehicles is very closely associated with the energy management strategy (EMS). In this paper, a practicality-oriented adaptive EMS for a parallel hybrid electric bus is presented, which combines the adaptive neuro-fuzzy inference system (ANFIS) and equivalent consumption minimization strategy (ECMS). Considering the regular and fixed route of the city bus, the optimal control trajectories can be attained by the dynamic programming in advance. Using the rolling optimization method, a group of optimal equivalent factors is extracted from aforementioned control trajectories and used as the training samples. Then, a trained ANFIS model that produces the optimal equivalent factor online is constructed, showcasing striking superiority in self-learning and inference. By applying the derived equivalent factor in the framework of the ECMS, an adaptive energy management controller is available to achieve desirable power distribution online. Finally, the simulation and hardware in the loop (HIL) tests are used to validate the effectiveness and feasibility of the controller. The results demonstrate that, compared with other strategies, the fuel economy with the proposed strategy can be effectively improved.

An ANFIS-Based ECMS for Energy Optimization of Parallel Hybrid Electric Bus

Hybrid electric vehicles equipped with several different power sources can change operation mode to deal with different operating conditions to improve fuel economy and reduce exhaust emissions. Due to the significant difference in the characteristics of the power sources and the discontinuity in the dynamics of the clutch, noticeable torque fluctuations occur during mode transition, which may result in system shock and an uncomfortable riding sensation. Based on the idea of a switched system, we proposed a three-stage control strategy to facilitate a smooth transition from electric mode to hybrid driving mode. In this coordination control strategy, a fuzzy-proportional–integral mixed controller was used to manipulate engine speed and an adaptive sliding mode controller was applied to control the electric motor torque for compensating for torque disturbances. Furthermore, the proposed control strategy was validated on hardware-in-the-loop simulation platform. The results demonstrate the effectiveness of t...

Mode transition coordination control for parallel hybrid electric vehicle based on switched system

A critical challenge in the energy management of hybrid electric vehicles is how to intelligently distribute the power between engine and electric motor with a significant reduction in fuel consumption and emissions. In this paper, iterative dynamic programming (IDP) and adaptive neurofuzzy inference system (ANFIS)-based energy management strategy was proposed for a parallel hybrid electric bus. First, IDP was used to obtain the optimal control trajectories for a specific driving cycle. During the iterative process, a modified gear-shifting strategy was introduced to achieve a good tradeoff between the fuel economy and the drivability performance. Next, multi-ANFIS networks were designed and trained to learn the control law from the available optimal trajectories. Finally, the real-time energy management controller based on the IDP-ANFIS was built to coordinate the output of the two power sources, which reduces the fuel consumption and emissions. The simulation and experimental results reveal that the IDP-ANFIS is feasible, and the performance is superior to that of the equivalent consumption minimization strategy and rule-based method.

Design of an Energy Management Strategy for a Parallel Hybrid Electric Bus Based on an IDP-ANFIS Scheme

The invention discloses a birotor motor driving system integrating electrical accessories and used for an electric automobile. The birotor motor driving system comprises a birotor motor, a power supply system, an auxiliary power system and a control system. The power supply system is electrically connected with the birotor motor and controls the birotor motor to rotate, the birotor motor is in transmission connection with the auxiliary power system and controls the auxiliary power system to run, and the control system is respectively in data connection with the birotor motor, the power supply system and the auxiliary power system. The birotor motor driving system integrating the electrical accessories and used for the electric automobile can integrate a main driving motor, a steering pump, an inflating pump, an air conditioner and a generator assembly, so a pure power-driven power system shares one power source, the volume and mass of the pure power-driven driving system are reduced, energy conversion links between a driving component and the electrical accessories are reduced, and the integration level and energy conversion efficiency of the pure power-driven power system are improved.

Birotor motor driving system integrating electrical accessories and used for electric automobile

The utility model discloses a birotor motor drive system for electric automobile's integrated electrical accessory, including birotor motor, power supply system, auxiliary power system and control system, power supply system is connected and controls the birotor motor and rotate with birotor motor electricity, the birotor motor is connected with auxiliary power system transmission and is controlled the operation of auxiliary power system, control system is connected with birotor motor, power supply system, auxiliary power system data respectively. A birotor motor drive system for electric automobile's integrated electrical accessory can integrate to main drive motor, steering pump, inflating pump, air conditioner, generator assembly, makes power supply of pure electric power system sharing, has reduced pure electric drive system's volume and quality, has reduced the conversion of energy link between driver part and the electrical accessory, has improved pure electric power system's integrated level and energy conversion efficiency.

Xu Yiqiang

Papers

An ANFIS-Based ECMS for Energy Optimization of Parallel Hybrid Electric Bus

Mode transition coordination control for parallel hybrid electric vehicle based on switched system

Design of an Energy Management Strategy for a Parallel Hybrid Electric Bus Based on an IDP-ANFIS Scheme

Birotor motor driving system integrating electrical accessories and used for electric automobile

A birotor motor drive system for electric automobile's integrated electrical accessory