This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode power-split PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.

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A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

This paper presents a formalization of the energy management problem in hybrid electric vehicles and a comparison of three known methods for solving the resulting optimization problem. Dynamic programming (DP), Pontryagin’s minimum principle (PMP), and equivalent consumption minimization strategy (ECMS) are described and analyzed, showing formally their substantial equivalence. Simulation results are also provided to demonstrate the application of the strategies. The theoretical background for each strategy is described in detail using the same formal framework. Of the three strategies, ECMS is the only implementable in real time; the equivalence with PMP and DP justifies its use as an optimal strategy and allows to tune it more effectively. DOI: 10.1115/1.4003267

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A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles

The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.

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Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

An analytical derivation of the Equivalent Consumption Minimization Strategy (ECMS) for energy management of hybrid electric vehicles (HEVs) is presented, based on Pontryagin's minimum principle. The derivation is obtained using a generic formulation of the energy management problem in HEVs and is valid for any powertrain architecture. Simulation results obtained for a series HEV are also provided.

ECMS as a realization of Pontryagin's minimum principle for HEV control

Model predictive control power management strategies for HEVs: A review

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route

Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed on measured-drive data from an urban route with varying topography. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The lowest information case represents that the vehicle knows that it is being driven in a certain environment, e.g., city driving, and that the controller has been optimized for that type of environment. The second highest information level represents a vehicle equipped with a GPS combined with a traffic-flow information system. In the highest information level, the future power demand is completely known to the control system, hence, the corresponding optimal controller results in the minimal attainable fuel consumption. This paper showed that good performance (1%-3% from the minimal attainable fuel consumption) can be achieved with the lowest information case, with a time-invariant controller that is optimized to the environment. The second highest information level results in less than 0.2% higher consumption than the minimal attainable on the studied route. This means that it is possible to design a predictive controller based on information supplied by the vehicle-navigation system and traffic-flow-information systems that can come very close to the minimal attainable fuel consumption. A novel algorithm that uses information supplied by the vehicle-navigation system was presented. The proposed algorithm results in a consumption only 0.3% from the minimal attainable consumption on the studied route.

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

The potential for reduced fuel consumption of hybrid electric vehicles by the use of predictive powertrain control was assessed. The predictive control was based on information supplied by the vehicle navigation system. The assessment was done by evaluating the fuel consumption using three optimal controllers, each with a different level of information access to the driven route. The results indicate that, for an urban route with varying topography, the use of predictive control can significantly reduce the fuel consumption.

A new approach for system design of hybrid powertrains was demonstrated in a case study. The method is based on the following presumptions: The performance of a Hybrid Powertrain Concept (HPC) is evaluated using computer simulation; a HPC cannot be correctly evaluated without an Energy Management Strategy (EMS) for the energy buffer; the optimal EMS is different for each HPC. Dynamic programming was used to generate EMSs that were optimal for the vehicles intended traffic environment and for each given HPC, enabling evaluation of a large number of HPCs. Over-adaptation of the EMSs was avoided by using a stochastic drive cycle model. The final delivery is a competitive powertrain component sizing and the corresponding optimal EMS.

Improving System Design of a Hybrid Powertrain Using Stochastic Drive Cycles and Dynamic Programming

An energy management system (EMS) controls the energy flows in a vehicle by adapting pricing rules. In the EMS the price (Pm, P 2, PB 1 ) of the energy is variable dependent of the momentary supply of energy in a global energy system, i.e. the vehicle. Each auxiliary system (GEN, B, C) in the global energy system has in individual price limit, above which the auxiliary system (GEN, B, C) won't purchase any more energy. Some auxiliary systems (B) have variable price limits depending of those auxiliary systems parameters. The auxiliary systems (GEN, B, C) are represented in the EMS by activation agents (CA 1, CA 2, CAn, BA 1, BAn), which have different behaviour depending of what kind of auxiliary system they represent. Said activation agents (CA 1, CA 2, CAn, BA 1, BAn) control the energy flows in the global energy system. In the EMS the energy systems are divided into two categories; energy main system and energy auxiliary systems.

Mattias Åsbogård

Papers

Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

Assessing the potential of predictive control for hybrid vehicle powertrains using stochastic dynamic programming

Improving System Design of a Hybrid Powertrain Using Stochastic Drive Cycles and Dynamic Programming

Energy management system of a vehicle