Parallel Plug-in Hybrid Electric Vehicle Research Articles

Online battery aging calculation in plug-in hybrid electric vehicle considering driving condition

ABSTRACT Online calculation of battery capacity loss and its instantaneous remaining life for electric and hybrid electric vehicles is a challenging subject, particularly for designing and optimizing vehicle energy management in real-life driving conditions. This paper presents an online method for estimating plug-in hybrid electric vehicle battery degradation, considering the driving conditions. For this purpose, a national parallel plug-in hybrid electric vehicle is employed, where its components have been sized based on some experimental map characteristics using a genetic algorithm. Also, a real-world driving cycle is developed based on the data gathered in the actual traffic conditions in Tehran. The vehicle’s energy management system is designed using an adaptive equivalent consumption minimization strategy. In addition, an empirical battery life model combined with an online cycle counting method is used for battery aging calculation. To evaluate the effectiveness of the proposed approach, the capacity fade of the vehicle battery over various driving cycles has been calculated. The comparison of the results confirms the efficacy of the proposed algorithm compared to the Rainflow counting algorithm. Applying the Rainflow to all driving cycles yields consistent outcomes. However, when employing the suggested method, capacity losses of approximately 2.4%, 1.9%, 2.1%, and 1.4% are detected in Tehran, FTP-75, WLTP, and NEDC, respectively, following one hundred driving cycles. This means the capacity loss increases by 35%, 50%, and 71% for FTP-75, WLTP, and Tehran, respectively, compared with the NEDC driving cycle.

Plug‐In Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous‐Discrete Reinforcement Learning

Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively.

Model Predictive Control of a Parallel Plug-In Hybrid Electric Vehicle Relying on Dynamic Programming and Extended Backward-Looking Model

Model Predictive Control of a Parallel Plug-In Hybrid Electric Vehicle Relying on Dynamic Programming and Extended Backward-Looking Model

Research on economical shifting strategy for multi-gear and multi-mode parallel plug-in HEV based on DIRECT algorithm

In response to the discrepancy between the mechanical-electric torque distribution rules used in the shifting calculation of plug-in hybrid electric vehicles and the actual operating conditions, this study focuses on a specific vehicle model known as the multi-gear and multi-mode parallel plug-in hybrid electric vehicle (MGMMP-PHEV). Various control parameters including vehicle speed, pedal opening, battery state of charge (SOC), and operating mode are taken into account by considering the impact of coordinated operation between the dual power sources and battery SOC. In order to address this issue, an enhanced dual-parameter shifting calculation principle and method are proposed, and an optimized shifting strategy is developed with the objective of achieving the highest system comprehensive efficiency and ensuring battery charge-discharge balance. The shifting points under each mode are optimized offline using the DIviding RECTangles (DIRECT) algorithm, and a multi-parameter fuzzy controller is introduced to dynamically adjust the shifting speed. The improved shifting strategy, implemented in Matlab/Simulink, is compared with the initial shifting strategy in terms of its economic performance. The results demonstrate that the DIRECT-optimized and dynamically adjusted fuzzy shifting strategy effectively maintains the balance of battery charge and discharge. Specifically, under the WLTC working condition, it achieves a 5.59 % reduction in fuel consumption compared to the dual-parameter shifting strategy. Furthermore, this optimized strategy leads to a slight increase in shifting delay and a decrease in shifting frequency, thus verifying the effectiveness and superiority of the proposed controller.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

Optimal Control of a PHEV Based on Backward-Looking Model Extended with Powertrain Transient Effects

The paper proposes a power flow control strategy for a P2 parallel plug-in hybrid electric vehicle (PHEV) which takes into account torque and power losses related to engine-on and gear shift transients. An extended backward-looking (EXT-BWD) model is proposed to account for the transient losses, while the control strategy combines a rule-based controller with an equivalent consumption minimization strategy. To describe the transient losses, the EXT-BWD model includes additional state variables related to engine on/off flag and gear ratio in the previous time step. To establish a performance benchmark for control strategy verification, a dynamic programming-based control variable optimization framework is established based on the EXT-BWD model. The proposed control strategy is demonstrated to improve the fuel efficiency and drivability compared to the original control strategy while retaining comparable computational efficiency.

Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-in Hybrid Electric Vehicle

In the current scenario, where environmental concern determines the evolution of passenger cars, hybrid electric vehicles (HEV) represent a hub in the automotive sector to reach net-zero CO2 emissions. To fully exploit the energy conversion potential of advanced powertrains, proper energy management strategies are mandatory. In this work, a simulation study is presented, aiming at developing a new control strategy for a P3 parallel plug-in HEV (PHEV). The simulation model is built on MATLAB/Simulink. The proposed strategy is based on an alternative utilization of the thermal engine and electric motor to provide the vehicle power demand (efficient thermal/electric skipping strategy (ETESS)). An adaptive function is then introduced to develop a charge-blended control strategy. Fuel consumption along different driving cycles is evaluated by applying the novel adaptive-ETESS (A-ETESS). To have a proper comparison, the same adaptive function is built on the equivalent consumption minimization strategy (ECMS). Processor-in-the-loop (PIL) simulations are performed to benchmark the A-ETESS. Simulation results highlighted that the proposed strategy provides for a fuel economy similar to ECMS (worse of about 2.5% on average) and a computational effort reduced by 99% on average, opening the possibility of real-time on-vehicle applications.

A Novel Learning-Based Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

The multisource electromechanical coupling renders the energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy-saving improvement of PHEVs. To address these issues, a novel learning-based model predictive control (LMPC) strategy is developed for a serial–parallel PHEV with the reinforced optimal control effect in real-time applications. Rather than employing the velocity-prediction-based MPC methods favored in the literature, an original reference-tracking-based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in the LMPC-based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance.

Real driving energy consumption and CO2 & pollutant emission characteristics of a parallel plug-in hybrid electric vehicle under different propulsion modes

PHEVs (Plug-in hybrid electric vehicles) are thought to be energy and environment friendly, while these conclusions are seldom verified during real driving tests. To evaluate PHEV real driving energy consumption, CO2 emission, and pollutants emission, one parallel PHEV was tested under real driving condition. The results indicate that the distance-specific energy consumption of CD (charge depleting) mode, compared with the CS (charge sustaining) mode, is 45% lower for WTW (Well to Wheel) evaluation. The CD WTW CO2 emission is 50% higher than the CS due to the electricity generation CO2 intensity. CD and CS mode could have similar CO2 emission when electricity CO2 intensity reaches 397.50 g/kWh. The limited charging reduced the PHEV energy conservation by around 50%, but the WTW CO2 emission is also reduced. Contrary to common belief, the PHEV CD mode real driving emission is underestimated, and it could be over 30 times higher than the CS mode. The deteriorated CD emission is caused by the inaccurate power demand prejudgments and frequent engine cold high-power re-start. The driving pattern comparison reveals that the deteriorated CD emission should not be a local but global concern. These results could be used for PHEV propulsion strategy optimization and test cycle design.

Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-In Hybrid Electric Vehicle

Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-In Hybrid Electric Vehicle

Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

Research on Adaptive Optimal Control Strategy of Parallel Plug-In Hybrid Electric Vehicle Based on Route Information

Analysis of optimal battery state-of-charge trajectory patterns for blended mode of a parallel plug-in hybrid electric vehicle and a wide range of driving conditions

Analysis of optimal battery state-of-charge trajectory patterns for blended mode of a parallel plug-in hybrid electric vehicle and a wide range of driving conditions

Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming

In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the dynamic programming algorithm is studied. The torque distribution rules and shifting rules are analyzed, and the optimal control strategy is adopted to design the control strategy, which effectively improves the fuel economy of plug-in hybrid electric vehicles. The fuel consumption rate of this parallel hybrid electric vehicle is based on china city bus cycle (CCBC) condition.

Energy saving performance improvement of intelligent connected PHEVs via NN-based lane change decision

Plug-in hybrid electric vehicles (PHEVs) unite the advantages of the engine and electric motor which could provide great potential in saving energy. However, the fuel economy performance of the PHEVs is highly associated with the driving condition, especially for parallel PHEVs because they could not decouple the engine work status from the driving condition. Meanwhile, fuel economy performance is not only a longitudinal issue but also related to lane selection. Lane selection is an important driving behavior and the algorithm of lane selection is necessary for the development progress of intelligent connected vehicles. Energy consumption cost is an important part of the vehicle’s using consumption cost. Therefore, lane selection strategies must consider this point. With the development of intelligent connected vehicle technology, such as V2X (Vehicle to Everything), the potential of energy consumption performance of intelligent connected PHEVs could be improved by taking environment information from V2X and smart sensors into lane selection. In this paper, a neural network (NN) based method is proposed to predict the future status of the local vehicle using the information from V2X, and then another network is used to estimate the future energy consumption of each lane. The lane selection is decided on energy consumption estimation. Lastly, the effectiveness of the method is validated by simulation using Matlab combined with SUMO (Simulation of Urban MObility).

Energy consumption analysis of a parallel PHEV with different configurations based on a typical driving cycle

Representative driving cycles and specific vehicle types play an important role in analyzing energy consumption to provide valuable references for manufacturers and government inspections. In this study, a parallel plug-in hybrid electric vehicle with different driven configurations is employed to analyze energy consumption under a typical driving cycle. Specifically, a typical driving cycle of Tianjin is constructed by integrating the clustering and Markov chain algorithms. Considering the various layouts of energy systems in plug-in hybrid electric vehicles, three different driven configurations (P2, P3, P4) of the parallel plug-in hybrid electric vehicle are established. Finally, the energy consumptions of the different configurations are analyzed under the proposed typical driving cycle and the compared China Automotive Test Cycle. Results of two driving cycles demonstrate that the fuel consumption of the P3 configuration is lower than that of the P2 and P4 configurations, respectively, while the purely electric driving range of the P3 configuration is longer than the other two configurations, respectively. These results provide a basis for the formulation of China’s new light vehicle emission standards and reduce exhaust emission by making the vehicles more economically suitable for the road conditions in Tianjin.

Effect of Engine Start and Clutch Slip Losses on the Energy Management Problem of a Hybrid DCT Powertrain

A Dynamic Programming (DP) formulation is developed to find the global optimal solution to the energy management of a parallel Plug-in Hybrid Electric Vehicle (PHEV) equipped with a Dual-Clutch Transmission (DCT). The effects of integrating in the DP formulation the losses accounting for gearshifts and engine starts are studied in terms of the overall fuel consumption; the optimal control solutions obtained depends on the occurrence of these transient events. These sources of dissipation are modeled through physical considerations thus enabling the DP algorithm to decide when it is more convenient, in terms of minimizing the total energy consumption, to perform either a gearshift or an engine start. This capability differentiates the DP formulation here presented from those presented in previous studies.

Catalysts for Post Euro 6 Plug-In Hybrid Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">Due to benefits from the use of electric power, Hybrid Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are regarded to be superior over conventional Internal Combustion Engine (ICE) only vehicles in fuel economy and emissions. However, recent studies find out that this is not always true. On certain conditions, hybrid vehicles can be even more polluted.</div><div class="htmlview paragraph">In order to identify these challenges and develop catalysts to meet more stringent emission requirement in the future, e.g. Euro 7, for hybrid application, as a part of our xHEV project, this study includes exclusively extensive investigation on a latest Euro 6d temp Parallel PHEV.</div><div class="htmlview paragraph">Key parameters including driving modes, Status of Charge (SOC), were investigated under World harmonized Light vehicles Test Cycle (WLTC) and Real Driving Emission (RDE) tests on the chassis dyno, and their influences on engine behaviours and emissions were studied, in comparison with emissions from the ICE only counterpart under the same legislation. Besides regulated emissions, secondary emissions and Particle Number (PN) and size distribution, including those under 23 nm were also analysed. It was found out cold start and hot restart emission control were critical for PHEV in order to fulfil our assumed Euro 7 legislation. Particle emissions were worst when the engine kicked in during the acceleration in the high-speed phase, i.e. cold catalyst under high space velocity exhaust and gas emissions were worst in the “Battery Control” mode when the ICE had to propel the vehicle while charging the battery.</div><div class="htmlview paragraph">Two new and improved aftertreatment systems were then proposed and evaluated that addressed the hybrid emission challenges without modifying the current vehicle calibration: The first was designed for overall best emission performance and the second was designed to meet the emission target at a lower cost. Both can reduce the hybrid vehicle emissions further and have the potential to meet the Euro 7 requirements for all gaseous and PN requirements.</div></div>

Optimal energy management and shift scheduling control of a parallel plug-in hybrid electric vehicle

This paper deals with design of a control strategy for a parallel powertrain configuration of a plug-in hybrid electric vehicle (PHEV). The control strategy is aimed at minimising fuel consumption and number of gear shift events for a wide range of driving cycles, while keeping the battery state-of-charge within allowable range. A control-oriented backward-looking model of PHEV powertrain is used as a design basis. The control strategy combines a rule-based controller with an equivalent consumption minimisation strategy (ECMS). The ECMS uses both transmission gear ratio and engine torque as control variables, thus eliminating a need for designing a separate gear shift scheduling strategy and exploiting a full potential of powertrain efficiency improvement. The overall control strategy is designed for different characteristic operating regimes including charge depleting, charge sustaining, and blended regimes. The strategy is verified by computer simulations against globally optimal benchmark obtained by using the dynamic programming-based optimisation, whose results are also used for fine tuning of controller parameters.

Optimal energy management and shift scheduling control of a parallel plug-in hybrid electric vehicle

Optimal energy management and shift scheduling control of a parallel plug-in hybrid electric vehicle

Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.