Commuter Route Optimized Energy Management of Hybrid Electric Vehicles

Larsson, Viktor; Mårdh, Lars Johannesson; Egardt, Bo; Karlsson, Sten

doi:10.1109/tits.2013.2294723

Cited by 83 publications

(36 citation statements)

References 18 publications

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“…In [172,173], two approaches to compute the reference state of charge of a plug-in HEV. Both approaches use logged data of velocity and altitude on a given route (that is assumed to be a commuting route).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Guanetti

Kim

Borrelli

2018

Annual Reviews in Control

526

260

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Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.

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Section: Literature Reviewmentioning

confidence: 99%

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

Guanetti

Kim

Borrelli

2018

Annual Reviews in Control

526

260

View full text Add to dashboard Cite

show abstract

“…There are, nonetheless, energy management problems where DP is tractable; it can, for example, be used to precompute an optimal strategy for a frequently driven route, such as a city bus route or a commuter route. Previous studies [9], [12] have shown that a near optimal fuel economy can be obtained by optimizing the energy management based on historical driving data logged along the route. The idea is then to solve the DP problem offline and use the resulting cost-to-go as feedforward information when the vehicle is driven along the route.…”

Section: Introductionmentioning

confidence: 99%

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

Larsson

Johannesson²,

Egardt

2015

IEEE Trans. Veh. Technol.

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103

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The computationally demanding Dynamic Programming (DP) algorithm is frequently used in academic research to solve the energy management problem of an Hybrid Electric Vehicle (HEV). This paper is focused exclusively 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 the paper: i) a local linear approximation, and ii) 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 the paper but the method is also applicable to an HEV. I. INTRODUCTIONDuring the last fifteen years significant attention has been given to the topic of optimal energy management for Hybrid Electric Vehicles (HEVs) and Plug-in HEVs (PHEVs). The task is non-trivial to solve since a priori information regarding the future driving conditions is needed. Furthermore, the plant model is generally non-linear with both continuous and integer decisions, i.e. torque split between engine/motor, choice of gear and engine on/off. Many different methods have been proposed for solving the energy management problem. Some examples are: rule based methods, Dynamic Programming (DP), convex optimization, and the Equivalent Consumption Minimization Strategy (ECMS) that is derived from the Pontryagin maximum principle. Refer to [1]-[3] for a review of different methods. This paper will focus exclusively on DP, which has been used in numerous studies [4]- [15]. The main advantage with DP is that it is a very versatile algorithm that can handle a wide range of problem formulations. It provides the global optimal solution, which cannot be guaranteed by a rule based method. In contrast to a convex optimization formulation, DP can handle integer decision variables without any need for approximations or iterative methods. Moreover, an important advantage compared to an ECMS strategy is that state constraints can be treated in a more formal way.

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“…The optimization-based method, as a natural extension of earlier methods, has attracted increasing attention [14][15][16][17]. One of the powerful optimization-based methods is dynamical programming (DP), which has illustrated the effectiveness in optimal energy management control for HEVs [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles

2015

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In this paper, a map-based optimal energy management strategy is proposed to improve the consumption economy of a plug-in parallel hybrid electric vehicle. In the design of the maps, which provide both the torque split between engine and motor and the gear shift, not only the current vehicle speed and power demand, but also the optimality based on the predicted trajectory of vehicle dynamics are considered. To seek the optimality, the equivalent consumption, which trades off the fuel and electricity usages, is chosen as the cost function. Moreover, in order to decrease the model errors in the process of optimization conducted in the discrete time domain, the variational integrator is employed to calculate the evolution of the vehicle dynamics. To evaluate the proposed energy management strategy, the simulation results performed on a professional GT-Suit simulator are demonstrated and the comparison to a real-time optimization method is also given to show the advantage of the proposed off-line optimization approach.

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Commuter Route Optimized Energy Management of Hybrid Electric Vehicles

Cited by 83 publications

References 18 publications

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

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

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

Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles

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