Extending Energy Management in Hybrid Electric Vehicles with explicit control of gear shifting and start-stop

Reeven, Vital van; Hofman, Théo; Huisman, R.G.M.; Steinbuch, M Maarten

doi:10.1109/acc.2012.6315200

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…Stop-Start Gear Selection [15] PMP PMP - [16] QP RB - [17] QP PMP RB [18] PMP DP DP [19] QP DP + cost DP + cost [20] MILP MILP + cost MILP + cost this work PMP DP + cost DP + cost By partitioning a large optimization problem into a set of smaller problems, as in a distributed control system [21], the computational efficiency and robustness are improved, albeit losing the guarantee of global optimality. The process of partitioning is not trivial and many different structures exist.…”

Section: Reference Power-splitmentioning

confidence: 99%

“…In these approaches, the cost of stop-start and gear change events are not considered, which can result in unacceptable switching behaviour, like hunting oscillations. To overcome this problem, costs on switchings are included, solved using Dynamic Programming (DP) and Quadratic Programming (QP) [19] or as one Mixed Integer Linear Program (MILP) [20]. For real-time solving, the allowed model complexity is limiting and adding additional states to these EMSs is computationally prohibitive.…”

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confidence: 99%

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Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Reeven

Hofman

2019

Vehicles

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The fuel economy of a hybrid electric vehicle (HEV) is improved, by taking the energy relevant system states into account in the energy management system (EMS). With an increasing number of states and decision variables, energy optimizing algorithms in the EMS can be prohibitive for real-time implementation. In part I of this work, a model-based, multi-level approach is taken to subdivide the original (large) optimization problem into computational efficient sub-problems, based on optimal control techniques using a preview. The resulting EMS solves the problem of power-split between engine and motor/generator, mode and gear switching including switching costs, with battery energy constraints. The superior energy efficiency of the multi-level EMS is simulated on a representative heavy duty drive cycle, where it saves 7.0% fuel, compared to a conventional vehicle, where the baseline EMS for the HEV saves 5.8%. In part II, real-world validation of the EMS is performed.

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Section: Reference Power-splitmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Reeven

Hofman

2019

Vehicles

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“…Introducing engine switching and gear selection decisions into the optimization problem is a significant challenge, however, as both require integer decision variables. A range of approaches have been taken, including mixed integer programming [6], Pontryagin's Minimum Principle (PMP) [7], convex relaxation [8], and genetic algorithms [9]. In [10] it was shown in simulations that the globally optimal solution can be obtained by alternating between a convex problem to determine the optimal power split for a fixed engine switching sequence, and PMP for the optimal engine switching given a fixed power split, and that this is generally faster than dynamic programming.…”

Section: Introductionmentioning

confidence: 99%

Fast Optimal Energy Management With Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

East

Cannon

2019

IEEE Control Syst. Lett.

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In this paper we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both power split and engine on/off decisions. The solution of a convex relaxation of the problem is used to initialize the optimization, which is necessarily nonconvex, and whilst only local convergence can be guaranteed, it is demonstrated that the algorithm will terminate with the optimal power split for the given engine switching sequence. The algorithm is compared in simulation against a charge-depleting/charge-sustaining (CDCS) strategy and dynamic programming (DP) using real world driver behaviour data, and it is demonstrated that the algorithm achieves 90% of the fuel savings obtained using DP with a 3000fold reduction in computational time.Index Terms-alternating direction method of multipliers, automotive control, energy management, optimization algorithms.

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“…Though DP can find the global optimum and handle non-convex nonlinear problems effectively, the optimality of its solution can be guaranteed only within the discretization accuracy of its states and controls. Other direct methods capable of solving MI problems to optimality are integer enumeration, branchand-bound, cutting planes, piecewise linear approximations, etc., [19]- [22]. All these methods are computationally too expensive due to the combinatorial nature of MI problems and suffer from large run-time variations.…”

Section: A Global Algorithms For Mixed-integer (Mi) Problemsmentioning

confidence: 99%

Numerical Strategies for Mixed-Integer Optimization of Power-Split and Gear Selection in Hybrid Electric Vehicles

Ganesan

Gros

Murgovski

2023

IEEE Trans. Intell. Transport. Syst.

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This paper presents numerical strategies for a computationally efficient energy management system that cooptimizes the power split and gear selection of a hybrid electric vehicle (HEV). We formulate a mixed-integer optimal control problem (MIOCP) that is transcribed using multiple-shooting into a mixed-integer nonlinear program (MINLP) and then solved by nonlinear model predictive control. We present two different numerical strategies, a Selective Relaxation Approach (SRA), which decomposes the MINLP into several subproblems, and a Round-n-Search Approach (RSA), which is an enhancement of the known 'relax-n-round' strategy. Subsequently, the resulting algorithmic performance and optimality of the solution of the proposed strategies are analyzed against two benchmark strategies; one using rule-based gear selection, which is typically used in production vehicles, and the other using dynamic programming (DP), which provides a global optimum of a quantized version of the MINLP. The results show that both SRA and RSA enable about 3.6 % cost reduction compared to the rule-based strategy, while still being within 1 % of the DP solution. Moreover, for the case studied RSA takes about 35 % less mean computation time compared to SRA, while both SRA and RSA being about 99 times faster than DP. Furthermore, both SRA and RSA were able to overcome the infeasibilities encountered by a typical rounding strategy under different drive cycles. The results show the computational benefit of the proposed strategies, as well as the energy saving possibility of co-optimization strategies in which actuator dynamics are explicitly included.

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Extending Energy Management in Hybrid Electric Vehicles with explicit control of gear shifting and start-stop

Cited by 16 publications

References 8 publications

Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Fast Optimal Energy Management With Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

Numerical Strategies for Mixed-Integer Optimization of Power-Split and Gear Selection in Hybrid Electric Vehicles

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