Hybrid Electric Vehicle System Research Articles

Bandwidth-Based Control Strategy for a Series HEV With Light Energy Storage System

The twin goals of maximizing fuel economy (FE) and improving consumer acceptance by reducing the cost of the energy storage system (ESS) in a series hybrid electric vehicle (SHEV) powertrain is addressed here by using energy storage as a means for filtering drive-cycle power demands on the engine, rather than an energy source for supplying an all-electric drive mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full-range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed that a 4.5-kWh lithium-ion battery pack was still required. In this paper, a sports-car-class SHEV was studied, where the challenge is to reduce the size of the ESS even more because the available space allocation is only one fourth that of the mid-size vehicle. In this paper, a controller is developed, which allows a hybridized SHEV to be realized with a light ESS. The controller includes a duty-cycling feature that manages the engine performance in multiple efficient regions and a bandwidth-limited (BWL) proportional controller feature that limits low-bandwidth battery current. The performance of the controller has been validated for a SHEV powertrain model with an 80- $\text{V}_{\mathrm{dc}}$ 1.125-kWh battery, plus an 80- $\text{V}_{\mathrm{dc}}$ 46.4-F ultracapacitor module using a customized Autonomie vehicle model. The results show that the combined FE of the new design is increased by 13%, compared with the corresponding FE in the equivalent conventional vehicle. Additional FE improvement is possible by reducing the engine size further to reflect the average power demand imposed by real drive cycles.

Dynamic energy management for a novel hybrid electric system based on driving pattern recognition

This paper focuses on the dynamic energy management for Hybrid Electric Vehicles (HEV) based on driving pattern recognition. The hybrid electric system studied in this paper includes a one-way clutch, a multi-plate clutch and a planetary gear unit as the power coupling device in the architecture. The powertrain efficiency model is established by integrating the component level models for the engine, the battery and the Integrated Starter/Generator (ISG). The powertrain system efficiency has been analyzed at each operation mode, including electric driving mode, driving and charging mode, engine driving mode and hybrid driving mode. The mode switching schedule of HEV system has been designed based on static system efficiency. Adaptive control for hybrid electric vehicles under random driving cycles with battery life and fuel consumption as the main considerations has been optimized by particle swarm optimization algorithm (PSO). Furthermore, driving pattern recognition based on twenty typical reference cycles has been implemented using cluster analysis. Finally, the dynamic energy management strategy for the hybrid electric vehicle has been proposed based on driving pattern recognition. The simulation model of the HEV powertrain system has been established on Matlab/Simulink platform. Two energy management strategies under random driving condition have both been implemented in the study, one is knowledge-based and the other is based on driving pattern recognition. The model simulation results have validated the control strategy for the hybrid electric vehicle in this study in terms of drive pattern recognition and energy management optimization.

Calibration methodology for energy management system of a plug-in hybrid electric vehicle

This paper presents a new analytical calibration method for energy management strategy designed for a plug-in hybrid electric vehicle. This method improves the actual calibration efficiency to reach a compromise among the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economic performance is constructed by using a radar chart method. A radial basis functions (RBFs) neural network model is proposed to establish a precise model among control parameters and the comprehensive evaluation indicator. The optimal Latin hypercube design is introduced to obtain the experimental data to train the RBFs neural network model. And multi-island genetic algorithm is used to solve the optimization model. Finally, an offline calibration example is conducted. Results validate the effectiveness of the proposed calibration approach in improving vehicle performance and calibration efficiency.

Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

This paper presents a calibration method of a rule-based energy management strategy designed for a plug-in hybrid electric vehicle, which aims to find the optimal set of control parameters to compromise within the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economy performance is constructed by the method of radar chart. Moreover, a radial basis functions (RBFs) neural network model is proposed to establish a precise model within the control parameters and the comprehensive evaluation indicator. The best set of control parameters under offline calibration is gained by the multi-island genetic algorithm. Finally, the offline calibration results are compared with the experimental results using a chassis dynamometer. The comparison results validate the effectiveness of the proposed offline calibrating approach, which is based on the radar chart method and the RBF neural network model on vehicle performance improvement and calibrating efficiency.

Battery Internal Temperature Estimation for LiFePO4 Battery Based on Impedance Phase Shift under Operating Conditions

An impedance-based temperature estimation method is investigated considering the electrochemical non-equilibrium with short-term relaxation time for facilitating the vehicular application. Generally, sufficient relaxation time is required for battery electrochemical equilibrium before the impedance measurement. A detailed experiment is performed to investigate the regularity of the battery impedance in short-term relaxation time after switch-off current excitation, which indicates that the impedance can be measured and also has systematical decrement with the relaxation time growth. Based on the discussion of impedance variation in electrochemical perspective, as well as the monotonic relationship between impedance phase shift and battery internal temperature in the electrochemical equilibrium state, an exponential equation that accounts for both measured phase shift and relaxation time is established to correct the measuring deviation caused by electrochemical non-equilibrium. Then, a multivariate linear equation coupled with ambient temperature is derived considering the temperature gradients between the active part and battery surface. Equations stated above are all identified with the embedded thermocouple experimentally. In conclusion, the temperature estimation method can be a valuable alternative for temperature monitoring during cell operating, and serve the functionality as an efficient implementation in battery thermal management system for electric vehicles (EVs) and hybrid electric vehicles (HEVs).

Design and Development of Real-Time Optimal Energy Management System for Hybrid Electric Vehicles

Design and Development of Real-Time Optimal Energy Management System for Hybrid Electric Vehicles

Efficient coordinated control of regenerative braking with pneumatic anti-lock braking for hybrid electric vehicle

Urban bus has to start and stop frequently due to typical urban traffic conditions, which, however, can be put to good use by regenerative braking. Regenerative braking is a key technology which not only improves vehicle’s fuel economy in mild braking, but also ensures vehicle safety in emergency braking conditions. Because of the inherent limitations of traditional braking system in recycling energy, it is necessary to change its structure to decouple the brake pressure and the brake pedal force. To solve this problem, a compromise design combining traditional pneumatic braking system with brake-by-wire (BBW) system is adopted in this paper on parallel hybrid electric bus. With the transformed braking system, an efficient coordinated control strategy is proposed to solve the problem caused by the different response speeds of pneumatic braking and regenerative braking. The proposed control strategy is carried out, where the road condition varies and different control methods are adopted. Results show that the adopted braking system and the proposed coordinated control strategy are suitable for different roads, and effective in recovering energy and ensuring vehicle safety. At the same time, shorter braking distance and better control of slip ratio verify the performance of MPC compared with a logical threshold-based control. Therefore, this study may offer a useful theoretical reference to the choice of braking system and braking control strategy design in hybrid electric vehicle (HEV).

Model predictive control power management strategies for HEVs: A review

Abstract This paper presents a comprehensive review of power management strategy (PMS) utilized in hybrid electric vehicles (HEVs) with an emphasis on model predictive control (MPC) based strategies for the first time. Research on MPC-based power management systems for HEVs has intensified recently due to its many inherent merits. The categories of the existing PMSs are identified from the latest literature, and a brief study of each type is conducted. Then, the MPC approach is introduced and its advantages are discussed. Based on the acquisition method of driver behavior used for state prediction and the dynamic model used, the MPC is classified and elaborated. Factors that affect the performance of the MPC are put forward, including prediction accuracy, design parameters, and solvers. Finally, several important issues in the application of MPC-based power management strategies and latest developing trends are discussed. This paper not only provides a comprehensive analysis of MPC-based power management strategies for HEVs but also puts forward the future and emphasis of future study, which will promote the development of energy management controller with high performance and low cost for HEVs.

Multi-objective evolutionary algorithm based on decision space partition and its application in hybrid power system optimisation

The distribution of individuals in a population significantly influences convergence to global optimal solutions. However, determining how to maximise decision space information, which benefits convergence, is disregarded. This paper proposes a type of multi-objective evolutionary algorithm based on decision space partition (DSPEA), and designs the sphere initialisation strategies, initialisation method of individuals in each sphere, updating approach for the centroid, radius, and individuals of a hypersphere, and information sharing mechanism among spheres. The decision space in the DSPEA framework is explicitly divided into several hyperspheres. The non-dominated sorting genetic algorithm II is employed to implement each evolution of each hypersphere. An improvement approach related to the information sharing of the spheres is used to produce the future motions of the spheres by adopting particle swarm optimisation. Twelve problems were used to test the performance of DSPEA, and extensive experimental results show that DSPEA performs better than six state-of-the-art multi-objective evolutionary algorithms. Finally, DSPEA is used to optimise a hybrid power system. The results of the simulation optimisation tests on the parameters of the control strategy and the drive system for hybrid electric vehicles demonstrate that the proposed approach can obtain a set of improved solutions with low fuel consumption and pollutant emission.

Kinetic energy storage using a dual-braking system for an unmanned parallel hybrid electric vehicle

In this paper a novel regenerative dual-braking strategy is proposed for utility and goods delivery unmanned vehicles on public roads, which improves their ability to recover regenerative energy and consequently improves the fuel use of parallel hybrid powertrain configurations for land unmanned vehicles where the priority is not comfort but extension of their range. Furthermore, the analysis takes into account the power-handling ability of the electric motor and the power converters. In previous research, a plethora of regenerative braking strategies have been reported; in this paper, the key contribution is that the vehicle electric regeneration is related to a fixed braking distance in relation to the energy storage capabilities specifically for unmanned utility-type land vehicles where passenger comfort is not a concern but pedestrian safety is of critical importance. Furthermore, the power converter capabilities of the vehicle facilitate the process of extending the braking time by introducing a variable-deceleration profile. The proposed approach has therefore resulted in a regenerative algorithm which improves the energy storage capability of the vehicle without considering the comfort since this analysis is applicable to unmanned vehicles. The algorithm considers the distance as the key parameter, which is associated with safety; therefore, it allows the braking time period to be extended, thus favouring the electric motor generation process while maintaining safety. This method allows the vehicle to brake for longer periods rather than for short bursts, hence resulting in more effective regeneration with reduced use of the dual system (i.e. the caliper–stepper motor brake system). The regeneration method and analysis are addressed in this paper. The simulation results show that the proposed regenerative braking strategy improved the ability of the hybrid powertrain configuration to recover energy significantly. The paper is also supported by experimental data that verify the theoretical development and the simulation results. The two strategies developed and implemented utilize a constant braking torque and a constant braking power. Both methods were limited to a fixed safety-based distance. Overall, the results demonstrate that the constant-braking-torque method results in better energy-based savings.

Blended Rule-Based Energy Management for PHEV: System Structure and Strategy

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop–starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper.

AMT downshifting strategy design of HEV during regenerative braking process for energy conservation

For hybrid electric vehicles (HEVs), regenerative braking might be the most effective way of energy conservation. However, the braking energy usually cannot be regenerated completely due to the limit of the motor maximum torque. When the braking torque provided by electric motor (EM) cannot meet the driver’s braking demand, the hydraulic brake system should provide extra braking torque, which results in braking energy loss. Therefore, it is meaningful adjusting EM work point to maximize the regenerative energy by transmission downshifting. In this paper, an HEV equipped with an automated manual transmission (AMT) is chosen as the study platform. First, simplified dynamic models of HEV system are built. Then, the process and advantages of AMT downshifting are analyzed and the characteristics of regenerative braking are obtained with different gear positions and different, of which two kinds of downshifting strategy are proposed on basis. At last, hardware-in-loop tests are carried out, and results show that the energy conservation of regenerative braking process with downshifting can be increased by 10.5–32.4% compared to that without downshifting.

Efficiency analysis of a bidirectional DC/DC converter in a hybrid energy storage system for plug-in hybrid electric vehicles

A bidirectional (Bi) DC/DC converter is one of the key components in a hybrid energy storage system for electric vehicles and plug-in electric vehicles. Based on the detailed analysis of the losses in the converter, this paper firstly develops a model to theoretically calculate the efficiency of the converter. Then, the influences of temperature, switching frequency, duty cycle and material of switching device on the converter’s efficiency are experimentally investigated. The analysis of the experimental results has shown that (1) The efficiency at the switching frequency of 15kHz is about 2% higher than that of 25kHz. (2) The efficiency at 25°C is similar to that at 85°C for the MOSFET SiC while the efficiency at 25°C is 2% higher than that at 85°C for the IGBT Si for both buck and boost modes. (3) In buck mode, when the duty cycles are decreasing from 66.7%, 50% to 33.33%, the peak efficiencies are also decreasing from 97.6%, 94.5% to 90.3%, respectively. In boost mode, when the duty cycle is increasing from 33.33%, 50% to 75%, the peak efficiency is decreasing from 96.9%, 96.5% to 92.4%, respectively. (4) The developed model can calculate the converter’s efficiency accurately

중형저상버스 병렬형 하이브리드화를 위한 동력전달계 용량매칭

그 동안 하이브리드 버스에 대한 연구로 플러그인 하이브리드, 직렬형, 병렬형 하이브리드 등에 대한 연구가 많이 진행되어져 왔다. 하지만 연구가 진행된 대부분의 차량들은 대형 버스이며 현재 국내에는 중형저상버스에 대한 연구는 전무한 실정이다. 또한 중형저상버스의 하이브리드화에 대한 연구 역시 미미한 실정이다. 본 논문은 MATLAB을 이용한 시뮬레이션을 통해 디젤 중형저상버스의 연비 평가를 수행하였으며, 이를 하이브리드화하였을 경우에 대한 최적의 용량 조합과 기어비를 제시하고 내연기관 시뮬레이션 연비 결과와 비교 분석하였다. 하이브리드화를 위한 구조로 전륜과 후륜이 독립적으로 동력을 전달하는 병렬형 하이브리드 시스템을 선택하였다. 동력원 용량 설계를 위해 목표 성능을 만족하는 요구파워를 계산하여 적용 가능한 동력원 용량 영역을 설계하였다. 설계 영역을 만족하는 각 단품들의 용량은 스케일링하여 구성하였으며, 엔진과 모터에 대한 동력 전달계의 용량 설계 알고리즘을 제시하고 동적 계획법을 이용하여 최적화를 수행하였다. 최종적으로 본 연구를 통해 내연기관 차량인 중형저상버스를 하이브리드화하였을 경우에 대한 연비 향상률과 최적의 동력원 용량, 기어비를 제시하였다. Most studies on hybrid buses are on large-sized buses and not mid-sized low-floor buses. This study uses MATLAB simulation to evaluate the fuel efficiency of such buses powered by diesel. Based on the results, a hybrid electric vehicle system is recommended for the best combination of power and gear ratio. A parallel hybrid system was selected for the hybridization, which transmits front and rear wheel power independently. The necessary power to satisfy the target performance was calculated, and the applicable capacity area was designed. Dynamic programing was used to create and optimize a component sizing algorithm, which was used to scale the capacity of each component of the power source to satisfy the design criteria. The fuel efficiency rate, optimum power source capacity, and gear ratio can be improved by converting a conventional bus into a parallel hybrid bus.

Improved Self-Tuning Fuzzy Proportional–Integral–Derivative Versus Fuzzy-Adaptive Proportional–Integral–Derivative for Speed Control of Nonlinear Hybrid Electric Vehicles

The objective of this paper is to identify the suitable advance controller among optimized proportional–integral–derivative (O-PID), improved self-tuning fuzzy-PID (ISTF-PID), advanced fuzzy nonadaptive PID (AF-NA-PID), and AF-adaptive PID (AF-A-PID) controllers for speed control of nonlinear hybrid electric vehicle (HEV) system. The conventional PID (C-PID) controller cannot tackle the nonlinear systems effectively and gives a poor tracking and disturbance rejection performance. The performances of HEV with the proposed advance controllers are compared with existing C-PID, STF-PID, and conventional fuzzy PID (C-F-PID) controllers. The proposed controllers are designed to achieve the desired vehicle speed and rejection of disturbance due to road grade with reduced pollution and fuel economy.

Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system

Efficiency and durability are becoming two key issues for the energy storage system in electric vehicles together with their associated power management strategies. In this paper, we present a procedure for the design of a near-optimal power management strategy for the hybrid battery and ultracapacitor energy storage system (HESS) in a plug-in hybrid electric vehicle. The design procedure starts by defining a cost function to minimize the electricity consumption of the HESS and to optimize the operating behavior of the battery. To determine the optimal control actions and power distribution between two power sources, a dynamic programming (DP)-based novel analysis method is proposed, and the optimization framework is presented accordingly. Through analysis of the DP control actions under different battery state-of-health (SoH) conditions, near-optimal rules are extracted. A rule based power management is proposed based on the abstracted rules and simulation results indicate that the new control strategy can improve system efficiency under different SoH and different SoC conditions. Ultimately, the performance of proposed strategy is further verified under different types of driving cycles including the MANHATTAN cycle, 1015 6PRIUS cycle and UDDSHDV cycle.

Model Predictive Control for Hybrid Electric Vehicle Platooning Using Slope Information

This paper presents a new model predictive control (MPC) system for hybrid electric vehicle (HEV) platooning using slope information to improve fuel economy. The new features of this study are as follows. First, a system for HEV platooning has been developed considering varying drag coefficients and road gradients. Second, the general model of the aerodynamic drag coefficient of different vehicles in a platoon is developed. Third, simulations and analysis (under different parameters, i.e., road conditions, prediction horizon, vehicle state of charge, etc.) are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. Fourth, the spacing between the vehicles in the platoon is designed in the objective function to ensure driving safety. The MPC problem is solved using a discrete numerical computation method: the continuation and generalized minimum residual method.

Fuzzy Logic-Based Fault Prevention of the Battery System in Hybrid Electric Vehicle

Due to high energy and power density, lithium-ion batteries (LIBs) have become one of the most popular choices for electric vehicle (EV) and hybrid electric vehicle (HEV) applications. However, reliability and durability of LIBs are highly affected by the operational and environmental conditions. The Battery Management System (BMS) is responsible for guaranteed performance and safety of batteries. Typically, the battery system protection against current or voltage fault conditions is performed using a simple on/off command from the BMS to a power contactor. However, the sudden elimination of power due to transient current or voltage changes (e.g. vehicle launch or climbing a slope) reduces the amount of energy that can be delivered and in some cases may be dangerous for the components in connection with the batteries. This paper presents the design of a closed-loop fuzzy-based control system to achieve maximum performance of the battery with regard to driving conditions such that safe operation of the battery is ensured. To do so, battery voltage or current is limited using a control input from a fuzzy controller implemented in the BMS. The vehicle Central Control Unit (CCU) corrects the traction motor torque command according to the control input received from the BMS in order to maintain battery safe operating region. This way, the battery current and voltage is limited before reaching the fault conditions, thereby improving the durability of the battery and preserving its life. Performance of the designed control system is evaluated in various driving regimes using a Hardware-in-the-Loop (HiL) test bench. Results show the efficacy of the designed control system toward the prevention of voltage and current fault conditions.

Combined Battery/Supercapacitor Hybridised Energy Storage Systems for Hybrid Electric Vehicles

Automakers are striving to increase electric vehicle driving range without compromising performance and simultaneously reducing charging times. However, this requires an increase of both the energy and power density of the Energy Storage System (ESS); though in battery design these two attributes are generally mutually exclusive therefore a compromise must be made. It is hypothesised that a Hybridised Energy Storage System (HESS) consisting of Supercapacitors (SC) and high energy density batteries in parallel with no DC/DC converter could result in an ESS with improved energy and power density in comparison to an ESS consisting of batteries only. To enable automotive engineers to design such systems we have created a toolset (i.e. models) that can accurately reproduce the behaviour of such a HESS, in particular, the interactions between the battery and SC. Further, the tool is valid over a range of conditions known to affect system performance such as temperature. The tools are also validated with experimental data where the individual contributions of the battery and SC can be assessed. A high-fidelity physically meaningful electrical Equivalent Circuit Model (ECM) of a supercapacitor is combined with a physical pseudo-3D thermal model. Separately, a physics-based battery model is generated and parameterised for an NCA 18650 cell. Model order reduction techniques are used to obtain an acceptable simulation time, resulting in a physics-informed electrical ECM which is subsequently coupled with a high-fidelity thermal model. Both models are individually validated across a range of operating conditions including temperature and current. The validated models are paired and the HESS model is validated over an automotive drive cycle with a particular emphasis placed upon battery current and temperature evolution. The generated toolset demonstrated that a HESS can provide superior performance to a battery only system for particular applications, however power availability limitations can cause highly detrimental effects for incorrectly sized systems, and hence good system design is critical. Figure 1

A Combined High-Efficiency Region Controller to Improve Fuel Consumption of Power-Split HEVs

An improved controller for the energy management system of a power-split hybrid electric vehicle (HEV) is developed with the objectives of minimizing fuel consumption and improving drivability. Considering the specific application of vehicles plying on scheduled trips such as public transport, this paper assumes that the controller is privileged with a priori knowledge of the estimated total tractive energy requirement and the duration of the journey. In comparison to a recently introduced constant high-efficiency region (CHER)-based controller, this paper demonstrates that further reductions in fuel consumption can be achieved under certain driving cycles by limiting the internal-combustion-engine (ICE) operation to a dynamically varying high-efficiency region and adopting state-of-charge (SOC) swing control for battery energy storage. The frequency of engine on/off is therefore directly decided by the size of the energy storage, allowable swing of the SOC, and the tractive energy required. Performances of the CHER and dynamic high-efficiency region (DHER) controllers are compared through simulations against the existing controller of a commercial vehicle. The results reveal that the DHER controller outperforms the other two controllers in terms of fuel consumption in highway-style-driving scenarios. Therefore, to minimize fuel consumption while improving drivability under all driving scenarios, this paper proposes to combine the CHER controller with the DHER controller such that the best features of both controllers can be utilized.