Hybrid Electric Vehicle System Research Articles

A Study of 1MHz Multisampling Deadbeat Control for Low Carrier Frequency PMSM Drive System

EVs (Electric Vehicles) and HEVs (Hybrid Electric Vehicles) have electric motor drive system, and its quick and smooth response is one of advantage in electric motor drive system. In order to utilize potential performance of control response, this paper introduces MSDB (Multi Sampling Deadbeat control) of PMSM (Permanent Magnet Synchronous Motor) drive system for EVs and HEVs using a FPGA (Field Programmable Gate Array). The proposed deadbeat control using a FPGA can realize good response and robustness, especially for electric motor drive system using low carrier frequency of PWM (Pulse Width Modulation) inverter like as EVs and HEVs.

Hierarchical Sizing and Power Distribution Strategy for Hybrid Energy Storage System

This paper proposes a hierarchical sizing method and a power distribution strategy of a hybrid energy storage system for plug-in hybrid electric vehicles (PHEVs), aiming to reduce both the energy consumption and battery degradation cost. As the optimal size matching is significant to multi-energy systems like PHEV with both battery and supercapacitor (SC), this hybrid system is adopted herein. First, the hierarchical optimization is conducted, when the optimal power of the internal combustion engine is calculated based on dynamic programming, and a wavelet transformer is introduced to distribute the power between the battery and the SC. Then, the fuel economy and battery degradation are evaluated to return feedback value to each sizing point within the hybrid energy storage system sizing space, obtaining the optimal sizes for the battery and the SC by comparing all the values in the whole sizing space. Finally, an all-hardware test platform is established with a fully active power conversion topology, on which the real-time control capability of the wavelet transformer method and the size matching between the battery and the SC are verified in both short and long time spans.

Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck

Accurate required power forecasting is critical to ensure the recharge mileage and to optimize the energy utilization of heavy-duty vehicles. This paper proposed an artificial intelligence model predictive control framework for the energy management system (EMS) of the series hybrid electric vehicle with terrain and load information. It aims to achieve optimal energy distribution with increased required power prediction accuracy and optimized SoC sequence by fast analyzing high-dimensional information. Firstly, the required power is predicted by a deep inference framework (LASSO-CNN), which integrates the least absolute shrinkage selection operator (LASSO) and convolutional neural network (CNN). Secondly, the optimal SoC sequence on hilly roads is planned in advance, with the SHEV's recuperated energy being predicted based on the current driving state. Finally, MPC-based predictive energy management is achieved by combining required power prediction and SoC planning with rolling optimization and feedback correction. Simulation results show that the fuel consumption is 2.51% lower than the deterministic dynamic programming-based controller, and the computation time is decreased by 48.1%. These promising results suggest that the proposed predictive energy management strategy can play a critical role in predicting power demand, which further reduces the fuel consumption of the hybrid electric mining truck.

Drive Control Strategy for Plug-in Hybrid Electric Vehicle System Based on Modular Multilevel Converter

A power conversion system for plug-in hybrid electric vehicles (PHEV) based on “back-to-back” modular multilevel converter(BMMC) is proposed to solve the problem that the traditional MMC-EV can only work with one motor. The power flow of BMMC system with two motors is complex and no one has ever studied it. The state of charge (SOC) balance control strategy of the integrated power converter is studied in various driving modes by this paper. Based on the bridge arm current control, this strategy combines the vector control of permanent magnet synchronous motor(PMSM) with the hierarchical SOC equalization control, realizes the high performance drive control of PHEV system, and high-efficiency power transfer and management among motor, generator and battery, and avoids the battery and energy loss caused by the redundant charge and discharge process. At the same time, it is convenient to switch freely between different driving modes. The correctness and feasibility of this control strategy are verified through RT-LAB real-time simulation.

Co-Optimization Scheme for the Powertrain and Exhaust Emission Control System of Hybrid Electric Vehicles Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. However, the optimization design for HEVs is complicated due to the presence of nonlinear dynamics and complicated integration of the HEV systems. Moreover, there is a trade-off between fuel optimization and emission reduction. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. The proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. Moreover, the ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. At first, the Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from a number of real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model employed for the optimization for both powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models, which are learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using the NN model, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

A multi-objective optimization energy management strategy for power split HEV based on velocity prediction

Under the complicated driving conditions, the sharp acceleration and deceleration actions would cause the high-rate charge and discharge current of electric driving system in hybrid electric vehicle (HEV), which brings about a serious impact on the battery lifetime. The hybrid energy storage system (HESS) combined with battery and ultracapacitor (UC), would be a possible solution to this problem. For HEV with HESS, in addition to improving fuel economy, realizing the protection of battery is also an important objective. However, improving one aspect performance may sacrifice another aspect performance. The tradeoff between multiple optimization objectives remains a challenge for energy management design. Aiming at this problem, a multi-objective optimization energy management strategy based on velocity prediction for a dual-mode power split HEV with HESS is proposed in this paper. Firstly, to get the precise predictive input sequence, generalized regression neural network (GRNN) is used to predict future velocity. Secondly, the power distribution of dual-mode power spilt HEV with HESS is described as a rolling optimization problem in the prediction horizon of model predictive control (MPC). A new cost function considering the fuel consumption and the protection of the battery is brought forward, and the optimization problem is solved using Pontryagin's minimum principle (PMP). Moreover, the Powell-Modified algorithm is introduced to execute the solving process of PMP. Finally, the proposed strategy is verified by comparing it with four other strategies under four different driving cycles. Compared to the rule-based strategy, the proposed strategy reduces root mean square (RMS) of battery current and fuel consumption by up to 18.5 % and 18.9 %, respectively.

A Two-Stage Dynamic Programming-Based Sizing of Hybrid Energy Storage System for Hybrid Electric Vehicles

A Two-Stage Dynamic Programming-Based Sizing of Hybrid Energy Storage System for Hybrid Electric Vehicles

Fuzzy logic-based voltage regulation of hybrid energy storage system in hybrid electric vehicles

Fuzzy logic-based voltage regulation of hybrid energy storage system in hybrid electric vehicles

A linear recursive state of power estimation method based on fusion model of voltage and state of charge limitations

As the main candidate of energy storage system for electric vehicles and hybrid electric vehicles, lithium-ion battery has attracted extensive attention. The working characteristics of the battery under dynamic stress stimulation are complex and changeable. To solve the problem of high-precision state of power estimation, a fusion model based on adaptive forgetting factor recursive least squares identification and voltage and charge state constraints was proposed, and a continuous discharge state of power analysis model for lithium-ion batteries was established. The adaptive forgetting factor recursive least square method based on battery model provides accurate and reliable online parameter identification feedback. The results show that the accuracy error of online parameter identification is less than 0.02V; the combination of the linear recursive algorithm of state of power analysis and the fusion model of voltage and current limit makes the power state estimation more reliable and accurate. The results show that when the battery is t=10s, the peak discharge power error is less than 80W.

Delay-Dependent MIMO Robust Control for Power Following System of Auxiliary Power Unit in Series Hybrid Electric Vehicles

In order to improve the transient and static performances of power following system in series hybrid electric vehicle (SHEV), this paper presents a delay-dependent MIMO robust control strategy. The control scheme coordinates the engine and the generator in the auxiliary power unit (APU) to reach the optimal speed and torque synchronously to output the required power. By the way of Pade approximation and multiple linear regression analysis, the delay time uncertainty in engine torque response is emphatically analyzed, which improves the accuracy of the engine torque model. The stability of the system is ensured theoretically by the Lyapunov Stability Theory and a sufficiently robust stability condition is established in terms of the linear matrix inequality (LMI). To avoid the problem of power reverse undershoot, a method of directly limiting the generator torque command is adopted, which is analyzed to not affect the stability of the system. The effectiveness and practicability of the proposed control strategy are demonstrated through numerical simulations and experiments.

Control of Hybrid Energy Storage System for Hybrid Electric Vehicles using Uniform Continuous Higher Order Sliding Mode Control

The goal of this paper is to control a fully active hybrid energy storage System (HESS) with a battery and super capacitor (SC) by introducing an uniform higher order sliding mode control. The system parameters to be tracked are the battery and SC current. To track the reference values of battery and SC currents, an uniform continuous higher order sliding mode controller (UCHOSMC) is applied which is a combination of two controllers one is an uniform super twisting control for disturbance rejection and other is uniform time convergence for a disturbance free system. Current references to be trackedare generated by a rule-based energy management system, the stability of the system is validated using Lyapunov stability analysis. The simulation results show the robustness of the controller with an effective tracking of the reference values under different load conditions.

A Brief Introduction to a Novel High-Efficiency Hybrid Power System for Hybrid Electric Urban Light Commercial Vehicles

A Brief Introduction to a Novel High-Efficiency Hybrid Power System for Hybrid Electric Urban Light Commercial Vehicles

Research on the Coordinated Control of Regenerative Braking System and ABS in Hybrid Electric Vehicle Based on Composite Structure Motor

An antilock braking system (ABS) can ensure that the wheels are not locked during the braking process which is an important system to ensure the safety of braking. Regenerative braking is also a crucial system for hybrid vehicles and helps to improve the cruising range of the car. As such, the coordinated control of a braking system and an ABS is an important research direction. This paper researches the coordinated control of the regenerative braking system and the ABS in the hybrid vehicle based on the composite structure motor (CSM-HEV). Firstly, two new braking modes which are engine-motor coordinated braking (EMCB) and dual-motor braking (DMB) are proposed and the coordinated control model of regenerative braking and ABS is established. Then, for the purpose of optimal operating efficiency and guaranteeing the vehicle brake slip rate, a braking force distribution strategy based on predictive control algorithm is proposed. Finally, the Simulink model is established to simulate the control strategy. Results show that the slip rate can well track the target and ensure the efficient operation of the system. Compared with the normal braking mode, the braking energy recovery rate of EMCB is similar, but it can reduce the fuel loss of the engine during the braking process by 30.1%, DMB can improve the braking energy recovery efficiency by 16.78%, and the response time to track target slip is increased by 12 ms.

Analysis of Hybrid Energy Storage System for Hybrid Electric, Battery Electric and Plugin Hybrid Electric Vehicles using Bidirectional DC/DC converter

This paper deals with the Hybrid Energy Storage System (HESS) for Battery Electric, Hybrid and Plug-in Hybrid Electric Vehicles. Its performance is compared with conventional HESS design and also only Battery design, conventional design uses a bigger dc/dc converter between Battery and Ultracapacitor to satisfy the peak power demands in the real time, In this analysis a smaller dc/dc converter is used which maintains the voltage of ultracapacitor higher than the battery voltage by working as controlled energy pump. Battery will provide power directly only when voltage of the Ultracapacitor drops below the voltage of the Battery. Therefore, a constant and smooth load profile can be created for the battery. For the proposed HESS system Simulation results are presented to verify.

Battery Aging-Aware Online Optimal Control: An Energy Management System for Hybrid Electric Vehicles Supported by a Bio-Inspired Velocity Prediction

In this manuscript we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed by the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances.

Efficiency Optimization of IPOP DC/DC System for HEV

Input parallel output parallel (IPOP) DC/DC system is widely utilized in many occasions like data center and hybrid electric vehicle (HEV). However, the efficiency characteristics of different converters usually differs from each other, and the conventional control strategy has difficult to realize efficient conversion of overall power of the system. To address this issue, Buck converter is taken as the module and whose efficiency model under current continuous conduction mode (CCM) is presented. Meantime, parameter identification method based on least square (LS) algorithm is proposed as well. In order to realize the efficiency optimization of the whole parallel system, the constraint conditions of each module are analyzed, and the current distribution calculation method based on sequential quadratic programming (SQP) algorithm for the parallel system is introduced. On this basis, a control strategy of proportional current distribution (PCD) is proposed, which not only ensures that the parallel system can always run as efficiently as possible under different loads, but also realizes the flexible switching between the current-sharing mode and current distribution mode. Finally, the simulation and experimental results prove that the proposed method can achieve 4.46% higher efficiency than conventional method within the given power range of each converter, and realize the efficiency optimization of the IPOP DC/DC system to the greatest extent.

Research on Matching of Power Transmission System of Electro-Hydraulic Hybrid Electric Vehicle

Considering the high power density and high recovery efficiency of the hydraulic energy storage system, the power system combined with the hydraulic pump is adopted. The matching of automobile power transmission system includes the matching of hydraulic pump parameters, hydraulic accumulator parameters, motor parameters and battery parameters. AMESim and Simulink were used to carry out simulation modeling to analyze its performance. The simulation results showed that the adaptive combination of hydraulic system and battery system in electro-hydraulic hybrid vehicle could effectively improve the economic performance of the vehicle while ensuring the dynamic performance of the vehicle. It puts forward more efficient technology concept for the future development of hybrid electric vehicle.

Exergoeconomic Assessment of the Ejector-Based Battery Thermal Management System for Electric and Hybrid-Electric Vehicles

Exergoeconomic Assessment of the Ejector-Based Battery Thermal Management System for Electric and Hybrid-Electric Vehicles

Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles

A novel control strategy for the adaptive real-time energy management of a commuter pull-in hybrid vehicle is proposed. The proposed strategy can adapt to various driving conditions so that fuel economy can be improved further in practice. Its main feature is that a fuzzy inference system (FIS) for online estimation of the reference SOC and an adaptive update law with traffic recognition are blended into the main frame of an adaptive-equivalent consumption minimization strategy (A-ECMS). The FIS is established through an adaptive neuro-fuzzy inference system (ANFIS) that is offline trained by the traffic information extracted from historical traffic data and the reference state of charge (SOC) optimized by dynamic programming (DP). The adaptive update law with traffic recognition means that the adaptive equivalent factor (A-EF) of the real-time A-ECMS is adjusted online according to the traffic information in the real route besides the SOC of the vehicle battery. This is because the initial A-EF and the proportional–integral coefficients of the A-EF adjuster are mappings of the SOC and the traffic road segment, and the mappings are optimized by particle swarm optimization (PSO) according to the different initial SOC and the real historical driving cycles of each segment. The proposed strategy is carried out on the simulation test platform integrated GT-Suite simulator and MATLAB/Simulink. The simulation results show that the proposed strategy can reach an optimal energy distribution on a near global optimal level (close to the level of dynamic programming (DP) under the deterministic driving condition). Compared with a rule-based (RB) strategy, the traditional ECMS, an A-ECMS with the linear SOC reference, an A-ECMS with the EF optimized by PSO and an A-ECMS with the A-EF adjusted by a fixed PI feedback controller of the SOC, the fuel consumption is reduced by an average of 22.98% 10.26% 6.52% 2.33% and 5.91% respectively.

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

The hybridization of vehicles is a viable step toward overcoming the challenge of the reduction of emissions related to road transport all over the world. To take advantage of the emission reduction potential of hybrid electric vehicles (HEVs), the appropriate design of their energy management systems (EMSs) to control the power flow between the engine and the battery is essential. This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. The review is carried out subject to the following idea: although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, HEVs continue to rely on well-known rule-based (RB) strategies. The contribution of this work is to present a quantitative comparison of the works selected. Since several studies do not provide results of their models against commercial RB strategies, it is proposed, as another contribution, to complete their results using simulations. From these results, it is concluded that the improvement of the analyzed EMSs ranges roughly between 5% and 10% with regard to commercial RB EMSs; in comparison to the optimum, the analyzed EMSs are nearer to the optimum than commercial RB EMSs.