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

Model predictive control power management strategies for HEVs: A review

Routing a mixed fleet of electric and conventional vehicles

Control of connected and automated vehicles: State of the art and future challenges

The highest control layer of a (hybrid) vehicular drive train is termed the Energy Management Strategy (EMS). In this paper an overview of different control methods is given and a new rule-based EMS is introduced, based on the combination of Rule-Based and Equivalent Consumption Minimization Strategies (RB-ECMS). The RB-ECMS uses only one decision variable and requires no tuning of many threshold control values and parameters. This decision variable represents the maximum propulsion power of the secondary power source (i.e. electric machine/battery) during pure electric driving. The RB-ECMS is compared with the strategy based on Dynamic Programming (DP), which is inherently optimal for a given cycle. The RB-ECMS proposed in this paper requires significantly less computation time with a similar result to DP (within 1% accuracy).

http://www.mate.tue.nl/mate/pdfs/7654.pdf

Rule-based energy management strategies for hybrid vehicles

This paper proposes a design method for the energy management strategy to explore the potential fuel saving of a hybrid electric vehicle (HEV) equipped with an automated manual transmission. The control algorithm is developed based on the combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP) to optimally control the discrete gearshift command, in addition to the continuous power split between the internal combustion engine and the electric machine. The proposed method outperforms DP in terms of computational efficiency, being 171 times faster, without loss of accuracy. Simulation results for a middle-sized HEV on the New European Drive Cycle show that, to further optimize the gearshift strategy, an additional fuel saving of 20.3% can be reached. Furthermore, with the start-stop functionality available, it is shown that the two-point boundary-value problem following from PMP cannot be solved with sufficient accuracy without loss of optimality. This means that the finding of a constant value for the Lagrange multiplier while satisfying the battery state-of-energy (SOE) at the terminal time is not always guaranteed. Therefore, an alternative approach of SOE feedback control to adapt the Lagrange multiplier is adopted. The obtained results are very close to the globally optimal solution from DP. Simulation results, including the start-stop functionality, show that the relative fuel saving can be up to 26.8% compared with the case of a standard gearshift strategy.

Optimal Control of the Gearshift Command for Hybrid Electric Vehicles

In this work the dynamic behavior and control of an automotive dry clutch is analyzed. Thereto, a straight-forward model of the clutch is embedded within a dynamic model of an automotive powertrain comprising an internal combustion engine, drivetrain and wheels moving a vehicle through tire-road adhesion. The engagement of the clutch is illustrated using the model best suited for simulation, based on work of Karnopp. These simulation results are used for conceiving a decoupling controller for the engine and clutch torque. Simulation results with the controller showing significant improvement over the un-controlled case in terms of vehicle launch comfort. A modified controller is proposed that results in even more appreciated drive comfort while not deteriorating other system behavior.

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Simulation and control of an automotive dry clutch

Fast and smooth clutch engagement control for dual-clutch transmissions

This paper aims at designing optimal gear shift strategies for conventional passenger vehicles equipped with discrete ratio transmissions. In order to study quantitatively an optimal trade-off between the fuel economy and the driveability, the vehicle driveability is addressed in a fuel-optimal gear shift algorithm based on dynamic programming by three methods: method 1, weighted inverse of power reserve; method 2, constant power reserve; method 3, variable power reserve. Furthermore, another method based on stochastic dynamic programming is proposed to derive an optimal gear shift strategy over a number of driving cycles in an average sense, hence taking into account the vehicle driveability. In contrast with the dynamic-programming-based strategy, the obtained gear shift strategy based on stochastic dynamic programming is real time implementable. A comparative analysis of all proposed gear shift methods is given in terms of the improvements in the fuel economy and the driveability. The variable-power-re...

A.F.A. Serrarens

Papers

Rule-based energy management strategies for hybrid vehicles

Optimal Control of the Gearshift Command for Hybrid Electric Vehicles

Simulation and control of an automotive dry clutch

Fast and smooth clutch engagement control for dual-clutch transmissions

Optimal gear shift strategies for fuel economy and driveability