Parallel Plug-in Hybrid Electric Vehicle Research Articles

Driving cycle development for Kuala Terengganu city using k-means method

Driving cycle plays a vital role in the production and evaluating the performance of the vehicle. Driving cycle is a representative speed-time profile of driving behavior of specific region or city. Many countries has developed their own driving cycle such as United State of America, United Kingdom, India, China, Ireland, Slovenia, Singapore, and many more. The objectives of this paper are to characterize and develop driving cycle of Kuala Terengganu city at 8.00 a.m. along five different routes using k-means method, to analyze fuel rate and emissions using the driving cycle developed and to compare the fuel rate and emissions with conventional engine vehicles, parallel plug-in hybrid electric vehicle, series plug-in hybrid electric vehicle and single split-mode plug-in hybrid electric vehicle. The methodology involves three major steps which are route selection, data collection using on-road measurement method and driving cycle development using k-means method. Matrix Laboratory software (MATLAB) has been used as the computer program platform in order to produce the best driving cycle and Vehicle System Simulation Tool Development (AUTONOMIE) software has been used to analyze fuel rate and gas emission. Based on the findings, it can be concluded that, Route C and single spilt-mode PHEV powertrain used and emit least amount of fuel and emissions.

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

An improved adaptive equivalent consumption minimization strategy for parallel plug-in hybrid electric vehicle

Energy management strategies are of the vital importance in fully playing the potential of plug-in hybrid electric vehicles. This paper proposes an improved adaptive equivalent minimization strategy for a parallel plug-in hybrid electric vehicle. In this method, performance of adaptive equivalent minimization strategy is prompted through incorporating information of future driving condition into equivalent factor adjustment. Two main works have been done. First, a novel equivalent factor adjustment method is proposed in the improved adaptive equivalent minimization strategy. Based on the predicted information of future driving condition, equivalent factor is tuned in each running loop of equivalent minimization strategy. Second, information of future driving condition is predicted by harnessing floating car data. Benefit from future driving condition forecasting, the improved adaptive equivalent minimization strategy holds better capability in accommodating road condition switching and preliminary self-regulation for hilly road. Simulation results show that, compared with the convention adaptive equivalent minimization strategy, the improved adaptive equivalent minimization strategy can obtain better fuel economy.

Adaptive Real-Time Energy Management Strategy for Plug-In Hybrid Electric Vehicle Based on Simplified-ECMS and a Novel Driving Pattern Recognition Method

This paper proposes an adaptive real-time energy management strategy for a parallel plug-in hybrid electric vehicle (PHEV). Three efforts have been made. First, a novel driving pattern recognition method based on statistical analysis has been proposed. This method classified driving cycles into three driving patterns: low speed cycle, middle speed cycle, and high speed cycle, and then carried statistical analysis on these three driving patterns to obtain rules; the types of real-time driving cycles can be identified according to these rules. Second, particle swarm optimization (PSO) algorithm is applied to optimize equivalent factor (EF) and then the EF MAPs, indexed vertically by battery’s State of Charge (SOC) and horizontally by driving distance, under the above three driving cycles, are obtained. Finally, an adaptive real-time energy management strategy based on Simplified-ECMS and the novel driving pattern recognition method has been proposed. Simulation on a test driving cycle is performed. The simulation results show that the adaptive energy management strategy can decrease fuel consumption of PHEV by 17.63% under the testing driving cycle, compared to CD-CS-based strategy. The calculation time of the proposed adaptive strategy is obviously shorter than the time of ECMS-based strategy and close to the time of CD-CS-based strategy, which is a real-time control strategy.

A New Efficient Fuel Optimization in Blended Charge Depletion/Charge Sustenance Control Strategy for Plug-In Hybrid Electric Vehicles

The fuel consumption and pollutant emissions of vehicles can significantly be reduced by adding an electric drivetrain to the internal combustion engine (ICE) without deteriorating the vehicle performance. One of the best suggestions to achieve such a goal is plug-in hybrid electric vehicles (PHEVs). In PHEVs, the design of an efficient energy management strategy plays a significant role. In this paper, a new control strategy is proposed for parallel PHEV to keep the ICE operation points close to the high-efficiency region, while minimizing the fuel consumption over the standardized drive cycles. First, a new high efficiency ICE on / off control strategy is designed. Second, the teaching-learning based optimization algorithm is used to optimize the proposed control strategy and find the optimal ICE operation region. In the rule-based controllers, the ICE operates within predefined constant efficiency region without incorporating the driving pattern. However, the proposed controller can optimally tune the ICE operation points based on each driving scenario to achieve a better fuel economy. Since the control strategy of PHEVs highly depends on the driving patterns, three different drive cycles with three different trip distances and one recharging opportunity during the trip are considered to show the effectiveness of the proposed technique in terms of the fuel economy and the ICE efficiency. The results illustrate that the proposed control strategy could greatly decrease the fuel consumption of the PHEV under all driving scenarios.

Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS

When searching for the optimal solution, Equivalent Consumption Minimum Strategy (ECMS) has to calculate and compare the total equivalent fuel rate of huge candidates covered all over the control domain for each time instant. Therefore, this strategy still has a heavy computation burden problem; it is a challenge for ECMS to be implemented online for real-time control. To reduce ECMS’s calculation load, this paper proposes an adaptive Simplified-ECMS-based strategy for a parallel plug-in hybrid electric vehicle (PHEV). A convex piecewise function is applied to fit the total equivalent fuel rate with respect to the motor torque, which is the control variable. Then, the ECMS problem is simplified to calculate and compare only five candidates’ total equivalent fuel rate to determine the optimal torque distribution. Particle swarm optimization (PSO) algorithm is applied to optimize the equivalent factor, and the MAPs of this factor under different driving cycles, driving distances and initial SOC are obtained. Based on this, the adaptive Simplified-ECMS-based strategy is proposed. Simulations were performed, and the results show that the Simplified-ECMS-based strategy can obviously shorten the calculation time compared to ECMS-based strategy, and the adaptive Simplified-ECMS-based strategy can decrease fuel consumption of plug-in hybrid electric vehicle by 16.43% under the testing driving cycle, compared to CD-CS-based strategy. A road test on the prototype vehicle is conducted and the effectiveness of the Simplified-ECMS-based strategy is validated by the test data.

Comparisons of Energy Management Methods for a Parallel Plug-In Hybrid Electric Vehicle between the Convex Optimization and Dynamic Programming

This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speed and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA) algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP) based method and the charging depleting–charging sustaining (CD/CS) method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC) values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save the fuel consumption and reduce the computation burden obviously.

Receding Horizon Optimal Control of PHEV with Demanded Torque Estimation Model

This paper proposes a real-time optimization method for the energy management of a plug-in hybrid electric vehicle (PHEV). Focusing on a parallel PHEV and the total energy consumption minimization of on-road driving operation, a suitable control scheme that combining a rule-based block for operation modes decision and a low-level model predictive control (MPC) algorithm to manage the transient operation during power assistance. During the transient operation, a model with time-varying parameter is introduced to generate the driving torque during the predictive horizon. By using the C/GMRES (Continuation/General Minimum Residual) based algorithm, the proposed nonlinear MPC problem is solved efficiently. Simulation results demonstrate the control performance.

Comparative study of different types of PHEV optimal control strategies in real-world conditions

With increasingly serious global environmental issues and energy shortages, energy conservation in transportation has become a significant, fundamental objective. The objective of the current research is to investigate the impacts of different types of optimal control strategies on the plug-in hybrid electric vehicle (PHEV) performance in real-world conditions. The optimal control strategies according to Pontryagin’s minimum principle (PMP) and optimized rule-based approaches are developed for the optimal pattern of a PHEV energy management system to reduce fuel consumption and emissions simultaneously, without sacrificing the vehicle performance. For this purpose, first, using test data for engine and battery, an experimental map-based model of the parallel PHEV is developed. Then, the powertrain components are sized by using a genetic algorithm (GA), over the real-world driving cycles. Subsequently, GA-fuzzy and PMP controllers are developed for energy management of the PHEV. Simulation results show the significant effectiveness of the proposed optimal control approaches on the fuel consumption and emissions reduction in various driving cycles. The convergence speed and global searching ability of PMP are significantly better than GA-fuzzy for the design of control strategy parameters. The sensitivity of battery initial state of charge, driving cycle, and road grade are analyzed on vehicle emissions and fuel consumption. The findings reveal that PMP could be adapted to different conditions by tuning co-state in a short time. This advantage makes it more adaptable to variation of real-world conditions. On the other hand, a fuzzy controller needs less computational effort and so is more appropriate for a certain condition.

Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction

Conclusive evidence has justified great importance of energy management strategies in the performance and economy of plug-in hybrid electric vehicles (PHEVs). This article pays attention to improve adaptive equivalent consumption minimization strategy (A-ECMS) for parallel PHEV based on driving behavior recognition and real time traffic information prediction. Three main efforts have been made to distinguish our work from exiting research. Firstly, a hierarchical driving behavior model is constructed, providing in-depth knowledge about behavior generation, transmission, and consequence. Secondly, an online driving behavior classification method is designed. The proposed method is the coefficient result of offline driving behavior study based on self-report driving behavior questionnaire (DBQ) and online driving behavior discrimination by BP neural network. Thirdly, an improved adaptive equivalent consumption minimization strategy (IA-ECMS) is formulated based on identified driving behavior and predicted real time traffic information. The IA-ECMS can realize equivalent factor tuning instantaneously and reasonably. The simulation results indicate the proposed energy management strategy holds potential in fuel economy improvement than A-ECMS.

A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle

The mode transition from electric propulsion mode to hybrid propulsion mode is important with regard to the power management strategy of plug-in hybrid electric vehicles (PHEVs). This is because mode transitions can occur frequently depending on the power management strategies and driving cycles, and because inadequate mode transitions worsen the fuel efficiency and drivability. A pre-transmission parallel PHEV uses a clutch between the internal combustion engine (ICE) and the electric motor (EM) to connect or disconnect the power source of the ICE for a mode transition. The mode transition requires additional energy consumption for clutch speed synchronization, and is accompanied by a drivetrain shock due to clutch engagement. This paper proposes a control strategy for the mode transition with gear-shifting to resolve the problems of energy consumption and drivetrain shock. Through the development of a PHEV performance simulator, we analyze the mode transition characteristics and propose a control strategy considering the vehicle acceleration and gear state. The control strategy reduces the duration required for the mode transition by moving the start time of the mode transition. This helps to improve energy efficiency while maintaining adequate drivability.

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.

Multi-objective component sizing of plug-in hybrid electric vehicle for optimal energy management

This paper presents a methodology for component sizing optimization of a parallel plug-in hybrid electric vehicle by considering it as a multi-objective optimization problem. In this approach, two objective functions are defined to minimize the drivetrain cost, fuel consumption, and exhaust emissions simultaneously. Also, the driving performance requirements are considered as constraints. In addition, fuzzy logic controller including blended control strategy is developed for the PHEV. Finally, by means of multi-objective particle swarm optimization algorithm, the best choices of components are selected for 32 miles of the both TEH-CAR and UDDS driving cycles. Simulation results demonstrate the effectiveness and practicality of the approach, which prepare different optimal component sizes with various drivetrain costs, equivalent fuel consumption, and exhaust emissions.

Li-ion battery performance and degradation in electric vehicles under different usage scenarios

Summary Lithium-ion (Li-ion) batteries are well known as an efficient energy storage solution for plug-in hybrid electric vehicles (PHEVs). However, performance and state of health of these batteries strictly depends on the usage scenario including operating temperature, power demand profile, and control strategy imposed by the battery management system. Also, in PHEVs equipped with electric climate control systems, climate control loads are imposed as additional loads on the battery, which results in a reduced all-electric range (AER) and increased battery capacity degradation. In this paper, vehicle AER, and fuel economy and life degradation of an aftermarket LiFePO4 Li-ion battery cell are studied for a PHEV under several usage scenarios. Each scenario consists of a series and parallel PHEV powertrain layout developed in Autonomie software, climate condition, that is, hot and cold weather, and a daily driving and charging profile. For simulations, models of battery performance, heat generation, and degradation developed based on experimental results are integrated with a thermal vehicle cabin model. Impact of climate control loads and battery thermal preconditioning are incorporated in the simulations. It is observed that climate control loads significantly affect the AER (up to 20%), fuel economy (up to 65%), and battery degradation (up to 25%). On the other hand, thermal preconditioning could be used to reduce these impacts. Copyright © 2015 John Wiley & Sons, Ltd.

Optimization of the component sizing for a plug-in hybrid electric vehicle using a genetic algorithm

This paper describes a methodology to optimize the component sizes of a plug-in hybrid electric vehicle in a blended control strategy using a genetic algorithm. For this purpose, first, a fuzzy logic controller with two operating modes is developed for the energy management system of a parallel plug-in hybrid electric vehicle. Then, the battery requirements to drive the vehicle during the all-electric range are determined. In order to optimize the transmission components, multi-objective constrained optimization based on a genetic algorithm is developed, where the objective function consists of the weighted fuel consumption and the weighted exhaust emissions. Also, the driving performance requirements of the vehicle are considered as the constraint functions from which, when they exceed their allowable bounds, the penalty functions are evaluated and added to the objective function. Finally, the results of optimization of the component sizing in a blended control strategy are compared for the optimized data and their default component sizes. The simulation results demonstrate the effectiveness of the approach and noticeable reductions in the equivalent fuel consumption and in the exhaust emissions while ensuring a good longitudinal vehicle performance in various driving cycles.

Analytic Solutions to the Dynamic Programming Subproblem in Hybrid Vehicle Energy Management

The computationally demanding dynamic programming (DP) algorithm is frequently used in academic research to solve the energy management problem of a hybrid electric vehicle (HEV). This paper is exclusively focused on how the computational demand of such a computation can be reduced. The main idea is to use a local approximation of the gridded cost-to-go and derive an analytic solution for the optimal torque split decision at each point in the time and state grid. Thereby, it is not necessary to quantize the torque split and identify the optimal decision by interpolating in the cost-to-go. Two different approximations of the cost-to-go are considered in this paper: 1) a local linear approximation and 2) a quadratic spline approximation. The results indicate that computation time can be reduced by orders of magnitude with only a slight degradation in simulated fuel economy. Furthermore, with a spline approximated cost-to-go, it is also possible to significantly reduce the memory storage requirements. A parallel plug-in HEV is considered in this paper, but the method is also applicable to an HEV.

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.

Comparison of Supervisory Control Strategies for Series Plug-In Hybrid Electric Vehicle Powertrains Through Dynamic Programming

This paper compares the optimal fuel and electricity costs associated with two supervisory control strategies for a series plug-in hybrid electric vehicle (PHEV) using dynamic programming. One strategy has no restrictions on engine fuel usage and the second is restricted to fuel usage only after the battery is depleted below a certain threshold. Both strategies are optimized using deterministic dynamic programming (DDP) to ensure a fair comparison. The DDP algorithm is implemented using a backward-looking powertrain model. Such an approach resolves the computational issues arising because of: 1) the interpolations required to obtain value function estimates and 2) the characterization of constraints through penalty functions. The primary conclusion is that there is no significant difference in the optimal performance of the two control strategies for the series PHEV except when gasoline is unreasonably cheap . This result contrasts sharply with previous controller performance results for parallel and power-split PHEVs where the two strategies' performance is shown to differ for any gasoline price and driving distance. The reason for the contrast is the flexibility of engine operation in a series PHEV. The results are examined for different relative fuel and electricity prices and trip lengths.

Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles

This paper proposes an optimal energy management strategy based on the approximate Pontryagin’s Minimum Principle (A-PMP) algorithm for parallel plug-in hybrid electric vehicles (HEVs). When the driving cycles are known in advance, the Pontryagin’s Minimum Principle (PMP) can help to achieve the best fuel economy, but real-time control has been unavailable due to the massive computational load required by instantaneous Hamiltonian optimization. After observing some regular patterns in numeric PMP results, we were inspired to apply a novel piecewise linear approximation strategy by specifying the turning point of the engine fuel rate for the Hamiltonian optimization. As a result, the instantaneous Hamiltonian optimization becomes convex. Considering the engine state, there are only five candidate solutions for the optimization. For the engine off state, only one of the available torque split ratios (TSR) is one of these five candidates. The other four TSR candidates are for the engine on state, including the TSR when the engine operates at the best efficiency point for the current speed, the TSR when the engine delivers all the required torque and two terminal TSRs. The optimal TSR is the one with the smallest Hamiltonian of the current engine state. The engine state with the smallest Hamiltonian will be requested for the next time step. The results show that the A-PMP strategy reduced fuel consumption by 6.96% compared with the conventional “All-Electric, Charge-Sustaining” (AE–CS) strategy. In addition, the A-PMP shortened the simulation time from 6h to only 4min, when compared with the numeric PMP method. Unlike other approximation methods, the proposed novel piecewise linear approximation caused no severe distortion to the engine map model. The engine state switching frequency is also reduced by 43.40% via both the filter and the corresponding engine on/off optimal control strategy.

Optimal Sizing of a Parallel PHEV Powertrain

This paper introduces a novel method for the simultaneous optimization of energy management and powertrain component sizing of a parallel plug-in hybrid electric vehicle (PHEV). The problem is formulated as a convex optimization problem to minimize an objective function, which is a weighted sum of operational and component costs. The operational cost includes the consumed fossil fuel and electrical energy, whereas the component cost includes the cost of the battery, electric motor (EM), and internal combustion engine (ICE). The powertrain model includes quadratic losses for the powertrain components. Moreover, the combustion engine and the electric motor losses are assumed to linearly scale with respect to the size and the losses of baseline components. The result of the optimization is the variables of the global optimal energy management for every time instant and optimal component sizes. Due to the dependency of the result on the driving cycle, a long real-life cycle with its charging times is chosen to represent a general driving pattern. The method allows the study of the effect of some performance requirements, i.e., acceleration, top speed, and all-electric range, on the component sizes and total cost.