Qiuming Gong

Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles

The plug-in hybrid electric vehicle (PHEV), utilizing more battery power, is considered a next-generation hybrid electric vehicles with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Global optimization charge-depletion power management would be desirable. However, this has so far been hampered due the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of Intelligent Transportation Systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, gas-kinetic base trip modeling approach was used for the highway portion trip and for the local road portion the traffic light sequences throughout the trip will be synchronized with the vehicle operation. Several trip models approaches were studied for a specific case. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control. The gas-kinetic based trip model for the highway portion can describe the dynamics of the traffic flow on highway with on/off ramps which may be missed by the model which used only the main road detectors data.

Optimal power management of plug-in HEV with intelligent transportation system

Hybrid electric vehicles (HEV) have demonstrated their capability of improving the fuel economy and emission. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of HEV. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. In the past, the trip information has been considered as future information for vehicle operation and thus unavailable a priori. This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board geographical information systems (GIS), global positioning systems (GPS) and advanced traffic flow modeling techniques. In this paper, a new approach of optimal power management of PHEV in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the final time of the cycle. The vehicle model was based on a hybrid SUV. Only fuel consumption is considered for the current stage of study. Simulation results showed significant improvement in fuel economy compared with rule-based power management. Furthermore, simulations on several driving cycles using the proposed method showed much better consistency in fuel economy compared to the rule-based control.

Power management of plug-in hybrid electric vehicles using neural network based trip modeling

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). Combined with the Intelligent Transportation Systems (ITS), our previous work developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for PHEV power management. Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The Gas-kinetic based model was used for the trip modeling in our previous study. The complicated partial deferential equation based model with several parameters needs to be calibrated had for implementation. In this paper, a neural network based trip model was developed for the highway portion, using the given data from WisTransPortal. The real test data was used for the training and validation of the network. The simulation results show that the obtained trip model using neural network can greatly improve the trip modeling accuracy, and thus improve the fuel economy. The potential of the advantages were indicated by the fuel economy comparison.

Trip Based Power Management of Plug-in Hybrid Electric Vehicle with Two-Scale Dynamic Programming

The plug-in HEV (PHEV), utilizing more battery power, has become the next-generation HEV with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Globally optimized charge-depletion power management would be desirable. However, this has so far been hampered due to the a priori nature of the trip information and the prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, charge-depletion control of PHEV is nearly globally optimized with a two-scale dynamic programming approach based on trip modeling with real-time and historical traffic data. For DP based charge-depletion control of PHEV, the SOC is desired to drop to a specific terminal value at the end of the trip. By specifying the origin and destination of a trip, the trip model, i.e. the driving cycle, is first obtained with the average of the historic traffic data, and the globally optimized SOC profile can be obtained by solving the overall or the macro-scale DP problem. The actual power management can be adapted during real-time vehicle operation with a micro-scale DP framework. The whole trip is divided into a number of segments, and for each segment, a smaller DP will be solved using the on-line traffic data transmitted to the vehicle from the traffic flow sensors within the segment. The SOC obtained in the macro-scale DP solution at the terminal location is reinforced to be the final value. Simulation study has been performed on a hybrid SUV model from ADVISOR, and a defined trip in the greater Milwaukee area. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control, and also the benefit of adaptation using the two-scale DP method.

Multi-information integrated trip specific optimal power management for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEV) are widely received as a promising means of green mobility by utilizing more battery power. Recently, we have proposed a scheme of two-scale spatial-domain dynamic programming (DP) as a nearly global optimization approach to trip based optimal power management for PHEV through the combination with traffic data and trip modeling. Previously, the segment-wise power demand and SOC change was calculated through numerical integration based on the average speed and acceleration of the segment, and lookup tables were obtained. When more parameters are involved into power management, such as road grade and load change, such process becomes very tedious. In this paper, the spatial-domain DP is improved by calculating the power demand and SOC change in an analytical manner. The power demand is first calculated based on length, initial speed, acceleration, road grade, payload and wind of a road segment. The SOC change is then calculated for different PSR. An adjustable segment scheme used of analytical function is developed in order to improve the computation efficiency of the optimal power management without losing much of fuel economy. Simulation study shows that incorporating additional trip information such as road grade and predictable payload change into the optimization can significantly improve the fuel economy. The computational efficiency is also evaluated. The proposed method can greatly facilitate the development of optimal power management strategy for PHEV with multiple information inputs.

Computationally efficient optimal power management for plug-in hybrid electric vehicles based on spatial-domain two-scale dynamic programming

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). We have recently developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for the PHEV power management, through the combination with the intelligent transportation systems (ITS). The trip models are obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. A computationally efficient algorithm is proposed in this paper to enhance the previously developed two-scale DP framework so that the computational efficiency meets the need of on-board implementation. The electric-vehicle mode is reinforced for predicted traffic stops. For the remainder of the trip, the route is divided into a number of segments of certain length. For each segment, the fuel consumption and SOC change are calculated in advance according to different level of power splitting ratio, speed and acceleration/deceleration. The optimization can then be solved in spatial domain, with much less dimension than that in time domain. Simulation study showed significant reduction of computational time with minor loss of fuel economy performance.

Trip Based Near Globally Optimal Power Management of Plug-in Hybrid Electric Vehicles Using Gas-Kinetic Traffic Flow Model

Abstract The plug-in hybrid electric vehicle (PHEV), utilizing more battery power, has become the next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. However, this has so far been hampered due the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of Intelligent Transportation Systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, gas-kinetic base trip modeling approach was used for the highway portion trip and for the local road portion the traffic light sequences throughout the trip will be synchronized with the vehicle operation. Several trip models approaches were studied for a specific case. For DP based charge-depletion control of PHEV, the SOC is forced to drop to a specific terminal value at the final time of the trip. Simulation study has been performed on a hybrid SUV model from ADVISOR, for the different trip modeling approaches. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control. The gas-kinetic based trip model for the highway portion can describe the dynamics of the traffic flow on highway with on/off ramps which may be missed by the model which used only the main road detectors data. The modeling approach shows a step to the more accurate trip model prediction which can be used for the power management of PHEV.

Computationally Efficient Optimal Power Management for Plug-In Hybrid Electric Vehicles With Spatial Domain Dynamic Programming

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). Combined with the Intelligent Transportation Systems (ITS), our previous work developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for PHEV power management. Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The main drawback was the dependency of external server for obtaining the macroscale SOC profile, which makes it difficult to handle the impromptu change of driving decision. In this paper, a computationally efficient strategy is proposed based on road segmentation and lookup table methods. Simulation results have shown its great potential for real-time implementation.Copyright