Hybrid Electric Vehicle System Research Articles

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

Adaptive Hierarchical Energy Management Design for a Plug-In Hybrid Electric Vehicle

To promote the real-time application of the advanced energy management system in hybrid electric vehicles (HEVs), this paper proposes an adaptive hierarchical energy management strategy for a plug-in HEV. In this paper, deep learning (DL) and genetic algorithm (GA) are synthesized to derive the power split controls between the battery and internal combustion engine. First, the architecture of the multimode powertrain is founded, wherein the particular control actions, state variables, and optimization objective are explained. Then, the hierarchical framework for control actions generation is introduced. GA is utilized to search the global optimal controls based on the powertrain model provided in MATLAB/Simulink. DL is applied to train the neural network model that is connecting the inputs and control actions. Finally, the effectiveness of the presented integrated energy management strategy is validated via comparing with the original charge depleting/charge sustaining policy. Simulation results indicate that the proposed technique can highly improve the fuel economy. Furthermore, a hardware-in-the-loop is conducted to evaluate the adaptive and real-time characteristics of the designed energy management system.

An Efficient Energy Management Scheme for a Hybrid FC-SC-Battery Electric Vehicle using Model Predictive Control and Multi-Objective Particle Swarm Optimization

This paper presents a new Design for an efficient Energy Management System (EMS) for a Plug-in Hybrid Electric Vehicle (PHEV) which is supplied by Fuel Cell/Super-capacitor/Battery sources to attain maximum energy savings and minimize the amount of the fossil fuel utilized by the transportation sector. Model Predictive Control (MPC) combined with Multi-Objective Particle Swarm Optimization (MOPSO) control strategy was proposed to manage the battery and the SC State of Charge (SOC) and decide the optimal power distribution between the Fuel Cell (FC), the Battery and the Super Capacitor (SC). The main target of the proposed EMS calculation technique is to achieve maximum energy efficiency, minimum battery current variation, minimum variation of the state of charge of the SC and minimum cumulative hydrogen consumption during the driving cycle. The proposed MOPSO based EMS methodology has been checked by the High Speed (US06) Driving Cycle utilizing the MATLAB/Simulink. Five diverse driving patterns are utilized to assess the speculation capacity of the developed technique. Simulation results and the real-time experiment demonstrate that the proposed MOPSO-Model Predictive Control based EMS technique can accomplish better energy effectiveness compared with Fuzzy Logic Rule-based EMS and GA-based EMS.

Power Management Strategy for Hybrid Energy Storage System in Hybrid Electric Vehicles

Power Management Strategy for Hybrid Energy Storage System in Hybrid Electric Vehicles

Improving the performance of thermal management system for electric and hybrid electric vehicles by adding an ejector

For electric and hybrid electric vehicles, a battery thermal management system (BTMS) is an essential component to maintain a proper operating temperature for the battery pack. In this paper, a modern BTMS involving an evaporator (for cooling passenger cabinet) and a chiller (for cooling battery pack) is investigated under different cooling temperatures. Three systems are studied in which (a) the refrigerant lines coming from the chiller and evaporator have the same temperature (for the basic system), or they have different temperatures to be connected by (b) the mixing chamber, or (c) an ejector for a suggested novel system. For a mixing-chamber based system, the results show that the refrigerant charge decreases by 12% compared to the basic system leading to improving the coefficient of performance (COP) by 4.7%. Alternatively, the ejector can efficiently be used instead of the mixing chamber. One of the advantages for the use of ejector is to increase the inlet pressure of the compressor, resulting in minimizing the required power input. Therefore, COP improvement is found to be in a range of 7.17%–77.9%, compared to the basic system under the investigated conditions. The refrigerant charge and the rejected heat by the condenser decrease by 12% and 14.4%, respectively.

Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty

In order to avoid the sharps and transients of power demand and extend the battery lifetime, three energy management strategies via wavelet transform (WT) considering temperature uncertainty for hybrid energy storage system (HESS) in the plug-in hybrid electric vehicle are proposed in this paper. The HESS consisting of batteries, ultracapacitors, along with two associated DC/DC converters is discussed and modeled in details. In addition, to further investigate the influence of temperature uncertainty, a random temperature variation and three-dimensional response surfaces are employed for modeling. To systematically compare the performances of WT-based (WTB) strategy, WT-and-rule-based (WTRB) strategy and WT-and fuzzy-logic-control-based (WTFLCB) strategy, an optimization scheme is presented directly. The simulation results demonstrate that the WTFLCB strategy shows better performance under temperature uncertainty. Moreover, a hardware in the loop experiment platform is set up to further verify the feasibility of the WTRB strategy for actual application. It is found that the battery SoC and ultracapacitor SoC estimation errors are less than 0.77% and 3.87%, respectively.

Design method considering the frequency band of the disturbance in the DC link voltage control system of hybrid electric vehicle driving systems

AbstractDC link voltage variable systems with DC/DC converter are used in hybrid electric vehicles for improving the power of the system by boosting the DC link voltage. Voltage feedback control using only proportional compensator has been proposed as a control method for the DC/DC converter. The control system can be easily designed, but there is a possibility that a steady‐state error occurs in the DC link voltage. In this paper, a proportional integral compensator is proposed to eliminate the steady‐state error of the DC link voltage in DC link voltage feedback controllers. In addition, when designing the gain of the compensator, the frequency band of the disturbance is considered. The proposed method is verified through experiments on the downsizing model.

Innovation design and optimization management of a new drive system for plug-in hybrid electric vehicles

A multi-mode coupling drive system has been designed and controlled to improve the dynamic characteristics and fuel economy of plug-in hybrid electric vehicles, which also can make full use of the configured superiority of centralized drive systems and distributed drive systems and avoid their structural defects. The configuration evolution process, working mechanism and drive modes of the multi-mode coupling drive system are introduced. The powertrain model is established for the target vehicle. Based on Charge Depleting-Charge Sustaining energy management strategy, an Electric Vehicle-Charge Sustaining energy management strategy is developed. The Improved Real-valued Genetic Algorithm is used to optimize the system structural and control parameters, it can help prioritize the drive modes which are based on the proposed energy management strategy. While ensuring the vehicle dynamics, the best energy allocation is achieved. The results show that comparing with a series distributed drive hybrid system and the intelligent Multi-Mode Drive (i-MMD) hybrid system under the NEDC condition, the 100-km fuel consumption of the optimized multi-mode coupling drive system is reduced by 16.52% and 15.40%. Respectively, it further proves the superiority of the drive system in improving vehicle economy.

A review on opportunities of thermionic regeneration system in hybrid electric vehicle

A hybrid electric vehicle utilizes power from both engine and battery to drive the wheels. Hybrid electric vehicle architectures are micro, mild, full and plug in hybrids. This review covers recent optimization in technologies and systems of hybrid electric vehicle. It is evident from the literature that utilizing waste energy such as braking losses for battery charging is more effective for micro and mild hybrid electric vehicle. The increase in electrification increases the demand of alternate battery charging methods in vehicle. Therefore, the opportunity lies in recovering waste heat of engine for battery charging. This could be done by direct energy converters such as thermoelectric and thermionic converters. The paper comprehensively studies these direct energy converters on the basis of their working principle and conversion efficiencies. The results from this study show that thermionic energy conversion stands better for hybrid electric vehicles as compared with other direct energy conversion methods.

ÇOK GİRİŞLİ DÖNÜŞTÜRÜCÜDEN OLUŞAN ŞARJ EDİLEBİLİR YAKIT HÜCRELİ BİR HİBRİT ELEKTRİKLİ ARAÇ İÇİN HESAPLAYICI VERİMLİ BİR ENERJİ YÖNETİM YÖNTEMİ

Fuel cell vehicle technology has drawn wide attention because of the environmental and economic issues related to excessive usage of fossil fuels. Fuel cells are known for their unidirectional environmental friendly operation; however, they have low power density and suffer from slow dynamics. Therefore, a sole fuel cell system cannot meet the requirements of an electric vehicle whose power demand is quite dynamic. In a way of hybridizing a fuel cell with energy storage devices, it can be possible to overcome aforementioned problems. A plug-in fuel cell hybrid electric vehicle system, equipped with a battery and an ultra-capacitor, is proposed in this work. In this system, a single multi-input converter is utilized to control source energies. Moreover, this work develops a computationally efficient energy management strategy which is essentially a frequency decoupling method basically taking the advantage of easily applicable low pass filters. In this strategy, a polynomial scales the fuel cell and battery power levels to regulate ultra-capacitor voltage. The whole system is tested via a simulation model after the detailed analysis of the multi-input converter.

An Investigation on the Control Strategies and Fuel Economy of a Novel Plug-In Hybrid Electric Vehicle System

In this paper, the configuration of a novel plug-in hybrid electric vehicle (PHEV) system is proposed. For the novel PHEV system, the main operating modes and the control strategy, including rules for mode switching and energy management, are established. A backward model of the vehicle involving the power sources, transmission, and control system is worked out in MATLAB/Simulink/Stateflow environment. The distribution of operating points of each power source, as well as the fuel consumptions of the vehicle under new European driving cycle (NEDC) and FTP75 testing cycles are simulated and compared with typical hybrid systems such as parallel, series, and Toyota hybrid system (THS). The results indicate that the proposed system can operate at high fuel economy by adopting the rule-based energy management strategy along with the equivalent consumption minimization strategy, which proves the feasibility and effectiveness of the configuration and control strategies.

Fuel consumption reduction by introducing best-mode controller for hybrid electric vehicles

In this paper, a power management system for hybrid electric vehicles is developed and shown to improve the vehicle fuel consumption in various working conditions. A best-mode concept is defined based on the results of the dynamic programming global optimization strategy. It is shown that the use of exclusive control relations for each working mode improves fuel saving. The working state of the engine and one electric motor is used to determine the best-mode of powertrain operation. The best-mode classification also considers the battery state of charge. This enables the controller to specify near optimal working points in a wide range of state of charge. The control rule for each different work-mode is developed based on the dynamic programming results and applying the particle swarm optimization algorithm. The results show that the best-mode controller is capable of achieving fuel consumptions around 97% of that of the offline dynamic programming.

Design of Parallel Braking Control System for Port Hybrid Electric Vehicle

ABSTRACT Zhang, Z., 2018. Design of parallel braking control system for port hybrid electric vehicle. In: Liu, Z.L. and Mi, C. (eds.), Advances In Sustainable Port And Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 513–519. Coconut Creek (Florida), ISS N 0749-0208. Conventional port hybrid vehicles can't effectively recycle tremendous kinetic energy brought by car brake due to the limitation of energy storage element and energy conversion form in energy recovery braking system. A parallel hydraulic hybrid power control system is designed to solve the contradiction between braking safety stability and realizing the maximum energy recovery. The linear proportional electromagnetic valve is adopted to control the hydraulic pressure of the hydraulic cylinder of the brake. The numerical control of the frictional braking force is realized, and the braking force control strategy can be realized with the braking control system. The experimental results show that the system improves the s...

Indirect engine sizing via distributed hybrid-electric unmanned aerial vehicle state-of-charge-based parametrisation criteria

This paper presents a design process for the challenging problem of sizing the engine pack for a distributed series hybrid-electric propulsion system of unmanned aerial vehicle. Sizing the propulsion system for hybrid-electric unmanned aerial vehicles is a demanding problem because of the two different categories of propulsion (the engine and the motor), and the electrical system characteristics. Furthermore, what adds to the difficulty is that the internal combustion engine does not directly drive the propellers, but it is connected to an electrical generator and therefore provides electrical power to the electric motors and propellers. Hence there is a clear distinction from the traditional engine solutions which are mechanically coupled to the propeller. This paper addresses this specific distinction and proposes an indirect solution based on properties on the electrical part of the system. In particular, a novel parametric characterisation engine sizing approach is presented using the battery pack state-of-charge during a realistic unmanned aerial vehicle flight scenario. Five candidate engine options were considered with different starting conditions for the electrical system. The results show that by using the state-of-charge properties it is possible to select an appropriate size of engine pack while carrying a suitable electrical propulsion pack. However, the solutions are not unique and are appropriate for given design criteria clearly indicated in the paper.

Development of Transmission Systems for Parallel Hybrid Electric Vehicles

This study investigated the matching designs between a power integration mechanism (PIM) and transmission system for single-motor parallel hybrid electric vehicles. The optimal matching design may lead to optimal efficiency and performance in parallel hybrid vehicles. The Simulink/Simscape environment is used to model the powertrain system of parallel hybrid electric vehicles, which the characteristics of the PIM, location of the gearbox at the driveline, and design of the gear ratio of a gearbox influenced. The matching design principles for torque-coupled–type PIM (TC-PIM) parameters and the location of the gearbox are based on the speed range of the electric motor and the internal combustion engine. The parameters of the TC-PIM (i.e., k 1 and k 2 ) are based on the k ratio theory. Numerical simulations of an extra-urban driving cycle and acceleration tests reveal that a higher k r a t i o has greater improved power-assist ability under a pre-transmission architecture. For example, a k r a t i o of 1.6 can improve the power-assist ability by 8.5% when compared with a k r a t i o of 1. By using an appropriate gear ratio and k r a t i o , the top speed of a hybrid electric vehicle is enhanced by 9.3%.

A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle

Battery State of Power (SoP) estimation is one of the most crucial tasks of the battery management system in electric and hybrid electric vehicles. The inevitable error in estimates of battery State of Charge (SoC) and State of Health (SoH) is a cause of inaccuracies towards estimating the SoP for an aged battery. To overcome this, the present study aims to propose a new approach for predicting an aged cell SoP in which no a priori knowledge of battery SoH is required and the estimation method is robust to inaccuracies of SoC estimates. Accordingly, a combined reference mode of constant-current and constant-voltage is utilized to estimate fresh cell SoP which is then adapted to various aging states using a model-less control system. The control system, which belongs to a class of fuzzy logic-based controllers, benefits form a closed-loop framework leading to a more reliable and accurate SoP estimate. For verification, an experimental setup comprised of fresh and aged LiFePO4 cell samples is designed and the extracted data are utilized in a Model-in-the-Loop simulation for a hybrid electric vehicle. The results demonstrate the improved accuracy and robustness of SoP estimation while achieving a guaranteed safe operation of battery.

Hybrid electric vehicle routing problem with mode selection

With the development of green logistics, logistics companies gradually are paying attention to the application of hybrid electric vehicles (HEVs). HEVs have the advantages of low energy consumption and pollution, while their disadvantage mainly lies in their limited continuous driving range. Therefore, it is necessary to optimize the use of fuel during the distribution process. We study the mode selection system in HEVs based on the background of green logistics and the above characteristics of HEVs. The mode selection system can adjust the driving mode of the HEV according to different road conditions to obtain the optimal use of fuel. In this paper, we propose a new study of a hybrid electric vehicle routing problem with mode selection. This problem is formulated as a mixed integer linear programming model. An improved particle swarm optimization algorithm (IPSO) is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method. The experimental results show that our proposed algorithm not only obtains the optimal solution for some small-scale problem instances and some medium-scale problems but also solves some large-scale situations (one hundred customers, eleven vehicles, eleven charging stations, eleven gas stations and four modes) within an hour.

MMC-Based SRM Drives With Decentralized Battery Energy Storage System for Hybrid Electric Vehicles

This paper proposes a modular multilevel converter (MMC) based switched reluctance motor (SRM) drive with decentralized battery energy storage system for hybrid electric vehicle applications. In the proposed drive, a battery cell and a half-bridge converter is connected as a submodule (SM), and multiple SMs are connected together for the MMC. The modular full-bridge converter is employed to drive the motor. Flexible charging and discharging functions for each SM are obtained by controlling switches in SMs. Multiple working modes and functions are achieved. Compared to conventional and existing SRM drives, there are several advantages for the proposed topology. A lower dc-bus voltage can be flexibly achieved by selecting SM operation states, which can dramatically reduce the voltage stress on the switches. Multilevel phase voltage is obtained to improve the torque capability. Battery state-of-charge balance can be achieved by independently controlling each SM. Flexible fault-tolerance ability for battery cells is equipped. The battery can be flexibly charged under both running and standstill conditions. Furthermore, a completely modular structure is achieved by using standard half-bridge modules, which is beneficial for market mass production. Experiments carried out on a three-phase 12/8 SRM confirm the effectiveness of the proposed SRM drive.

Multi-fidelity validation algorithm for next generation hybrid-electric vehicle system design

With the evolution of increasingly complex hybrid-electric vehicle powertrains, the process of creating validated system models has become progressively more difficult to achieve. Increasing levels of confidence in how instantaneous vehicle energy states are captured is needed to take full advantage of the fuel consumption and emissions reduction potential of the vehicle to move towards more sustainable transportation systems. While many strategies for model validation exist, the majority rely on ascertaining comparisons with global system characteristics, for instance, total fuel consumption over a fixed driving event. However, these methods do not necessarily account for the rapidly fluctuating energy states which need to be understood to optimise the vehicle’s energy management strategy. The current work proposes a new validation approach which captures these instantaneous characteristics taking advantage of the high signal sampling rates available from modern data acquisition equipment rather than relying on drive cycle average or cumulative global behaviours. The method proposed provides a holistic view of the behaviours demonstrated by the vehicle model and identifies regions of poor system validation targeting areas for further model refinement. The algorithm is demonstrated on a new post-transmission, parallel mild-hybrid-electric bus. The model was developed in the MATLAB Simulink modelling environment. The validation algorithm is tested against vehicle dynamometer and test track data. With an increasing volume of mild and full hybrid vehicle configurations emerging, validation strategies such as the one proposed here are increasingly important for the design of energy management strategies to deliver the full potential benefits of the vehicle. The algorithm is proposed in a step by step method which can be automated to limit required user input.

A Controller Design for a Stability Improvement of an Integrated Charging System in Hybrid Electric Vehicle

Abstract This paper proposes a controller design for a stability improvement of an integrated charging system in hybrid electric vehicle (HEV). Conventional HEVs include a starter generator system, which consists of generator and its drive inverter. Contrary to them, the proposed integrated charging system operates as a generator drive system and bi-directional battery charging system by installing some power relays and additional circuit in general HEVs. Therefore, the integrated charging system results in reduced system components in HEV by eliminating on-board charger (OBC), also the weight and system volume can be decreased. In addition, a feed-forward compensation method is proposed to improve the transient states characteristics of the battery charging system in this paper. The effectiveness and validity of proposed design and control method for the integrated charging system is verified by simulation results.