Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming

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Fältnamn	Indikatorer	Metadata
000		03965naa a2200457 4500
001		oai:DiVA.org:kth-330694
003		SwePub
008		230630s2024 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3306942 URI
024	7	a https://doi.org/10.1109/tiv.2023.32853922 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Liu, Tong,c PhD Candidateu KTH,Mekatronik och inbyggda styrsystem4 aut0 (Swepub:kth)u1hdpf6u
245	1 0	a Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming
264	1	b IEEE,c 2024
338		a print2 rdacarrier
500		a Not duplicate with DiVA 1753630QC 20230704
520		a Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin's minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via the parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and thus has higher fuel efficiency than a non-adaptive dynamic programming (DP) controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The concise data structure of the AVF enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Reglerteknik0 (SwePub)202022 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Control Engineering0 (SwePub)202022 hsv//eng
650	7	a TEKNIK OCH TEKNOLOGIERx Maskinteknikx Farkostteknik0 (SwePub)203032 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Mechanical Engineeringx Vehicle Engineering0 (SwePub)203032 hsv//eng
653		a Hybrid electric vehicle
653		a energy management strategy
653		a adaptive dynamic programming
653		a approximated value function
653		a Optimeringslära och systemteori
653		a Optimization and Systems Theory
653		a Industriella informations- och styrsystem
653		a Industrial Information and Control Systems
700	1	a Tan, Kaigeu KTH,Mekatronik och inbyggda styrsystem4 aut0 (Swepub:kth)u13ad7fk
700	1	a Zhu, Wenyaou KTH,Mekatronik och inbyggda styrsystem4 aut0 (Swepub:kth)u11i78hw
700	1	a Feng, Leiu KTH,Mekatronik och inbyggda styrsystem4 aut0 (Swepub:kth)u1farda5
710	2	a KTHb Mekatronik och inbyggda styrsystem4 org
773	0	t IEEE Transactions on Intelligent Vehiclesd : IEEEg 9:2, s. 4085-4099q 9:2<4085-4099x 2379-8858x 2379-8904
856	4	u https://doi.org/10.1109/tiv.2023.3285392y Fulltext
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-330694
856	4 8	u https://doi.org/10.1109/tiv.2023.3285392

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Av författaren/redakt...: Liu, Tong, PhD C ...; Tan, Kaige; Zhu, Wenyao; Feng, Lei

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Reglerteknik

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Maskinteknik; och Farkostteknik

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