A Dynamic Programming Algorithm for HEV Powertrains Using Battery Power as State Variable

Bruck, Lucas; Lempert, Adam; Bonab, Saeed Amirfarhangi; Lempert, Jeremy; Biswas, Atriya; Anselma, Pier Giuseppe; Roeleveld, Joel; Rane, Omkar; Madireddy, Krishna; Wasacz, Bryon; Belingardi, Giovanni; Emadi, Ali

doi:10.4271/2020-01-0271

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Assuming overall a priori knowledge of future driving conditions for the drive cycle under analysis, the global optimal solution for the full HEV control problem with constraints on smooth driving, battery SOC and battery SOH can be found by exploiting the Bellman's principle of optimality [28]. Deterministic DP can be implemented in this framework as a well-known procedure to evaluate global optimal HEV control trajectories [29,30]. In general, the optimal HEV control solution is evaluated by DP by exhaustively sweeping all possible discretized control actions while solving an optimization problem backwardly form the final time instant to the initial one of the considered driving mission [31,32].…”

Section: Baseline Dp Formulationmentioning

confidence: 99%

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Anselma

2022

Energies

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Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains.

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Section: Baseline Dp Formulationmentioning

confidence: 99%

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Anselma

2022

Energies

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“…Alternatives to these controllers are global optimization methods or offline School of Automotive Engineering, Iran University of Science & Technology, Tehran, Iran optimization-based systems. Dynamic programming (DP), 14 genetic algorithm (GA), 15 bee algorithm (BA), 16 and particle swarm optimization (PSO) algorithm 17 are examples of offline optimization-based controllers. These strategies need the whole driving cycle data and involve more computation time and load, and are not used in real-time 18 applications.…”

Section: Introductionmentioning

confidence: 99%

“…Some efforts have been made to reduce the computational time. Bruck et al 14 used the cumulative battery power vector with macro and micro discretization instead of battery state of charge (SOC) as the state variable. Yang et al 19 developed a rapid dynamic programming approach (Rapid-DP) that ensures a better balance between fuel optimality and computational time.…”

Section: Introductionmentioning

confidence: 99%

Introducing predictive Best-Mode controller for minimizing hybrid electric vehicle fuel consumption

Khademnahvi

Mashadi

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, a real-time predictive control strategy is developed to control the energy consumption of hybrid electric vehicles with lower sensitivity to prediction accuracy. A predictive Best-Mode concept is introduced based on the future speed predictions, by which the trend of battery state of charge is estimated. The estimated battery state of charge is used to better management of the battery charge mode. The optimum work zones of the components are then selected according to the best battery charging mode and the vehicle speed and power demand. This controller is less sensitive to the prediction accuracy and enables the system to work at the near-optimal points. The results show that the predictive Best-Mode controller is capable of minimizing the energy consumption in real-time applications, very close to the results of the offline dynamic programming with a 2% error margin. The predictive Best-Mode strategy's performance is better than the finite-horizon dynamic programming, except for accurate prediction with a longer than 20-sec prediction horizon.

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“…For the case of obtaining EMS, DP needs prior knowledge about the driving cycle. Similar to the way used in this work, DP can provide the benchmark solutions to assess the performance of other proposed methods for EMS [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

MPC-Based Energy Management Strategy for an Autonomous Hybrid Electric Vehicle

Bonab

Emadi

2020

IEEE Open J. Ind. Applicat.

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Despite the current intense research on each of the subjects of electrification and autonomous driving, potential advantages as a result of the interaction of these two mainstreams in automotive have not been effectively studied yet. Autonomous vehicles generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. In this article, we present a novel approach for improving the fuel economy of an autonomous hybrid electric vehicle by taking advantage of this data. We introduce the term of autonomous-specific energy management strategy (ASEMS) and we present an example of such a strategy using model predictive control (MPC). Specifically, we show how a more fuel-optimal energy management strategy (EMS) can be achieved for the power-split powertrain of an autonomous hybrid electric vehicle using the motion planning data. We use an optimization-based motion planning approach and feed the resulting velocity profile up to the prediction horizon to the MPC-based EMS. The presented approach shows 2% to 12.81% less fuel consumption for the two extreme cases of 100 and 1000 meters as the prediction horizons, compared to a rule-based EMS. The presented EMS fuel-optimality for the 1000 meters is only 6.91% sub-optimal compared to the globally optimal results of dynamic programming.

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A Dynamic Programming Algorithm for HEV Powertrains Using Battery Power as State Variable

Cited by 8 publications

References 11 publications

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Introducing predictive Best-Mode controller for minimizing hybrid electric vehicle fuel consumption

MPC-Based Energy Management Strategy for an Autonomous Hybrid Electric Vehicle

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