ORIGINAL PAPER
Optimizing the lifespan of lithium-ion batteries for piezoelectric devices using dynamic controllers and AI models
1
UM5 ENSAM Rabat, Morocco
Submission date: 2025-03-13
Final revision date: 2025-09-10
Acceptance date: 2025-09-16
Publication date: 2026-04-01
Polityka Energetyczna – Energy Policy Journal 2026;29(1):49-68
KEYWORDS
optimizing the lifespan of lithium-ion batteriespiezoelectric devicesmachine learningpowering biomedical devicescontrollers
TOPICS
ABSTRACT
Powering small biomedical and Internet-of-Things devices with piezoelectric harvesters is challenging because the available power is low, intermittent, and difficult to condition, which can actually overstress storage elements if charging is not adapted. This study evaluates lithium-ion battery longevity under such constraints using NASA PCoE datasets and designs charge policies matched to limited source power. Firstly, we examine temperature – voltage behavior, and identify a moderate operating window, which motivates a slow-charge baseline that reduces electrochemical stress. Secondly, we introduce two control layers: a dynamic, rule-based controller that derates current as terminal voltage approaches 4.2 V or temperature exceeds 24°C within a defined safe window, and intelligent controllers (Random Forest, XGBoost, Gradient Boosting) that predict incremental degradation from routinely measured signals and select the current that minimizes expected damage under the same constraints. Using the number of cycles to SoH = 0.7 as the endpoint, dynamic control extends life from 30 to 43 cycles, while the intelligent controllers reach 45, 47, and 48 cycles. Reduced voltage peaks and ripple, together with lower thermal exposure, support the mechanism. Overall, aligning slow, condition-aware, and predictive charging with piezoelectric availability robustly extends service life and improves energy-capture efficiency, enabling more reliable, lower-maintenance biomedical and Internet-of-Things systems.
CONFLICT OF INTEREST
The Authors have no conflicts of interest to declare.
METADATA IN OTHER LANGUAGES:
Polish
Optymalizacja żywotności baterii litowo-jonowych do urządzeń piezoelektrycznych przy użyciu kontrolerów dynamicznych i modeli sztucznej inteligencji
optymalizacja żywotności akumulatorów litowo-jonowych, urządzenia piezoelektryczne, uczenie maszynowe, zasilanie urządzeń biomedycznych, sterowniki
Zasilanie małych urządzeń biomedycznych i urządzeń Internetu rzeczy za pomocą piezoelektrycznych urządzeń zbierających energię stanowi wyzwanie, ponieważ dostępna moc jest niska, przerywana i trudna do uregulowania. Może to w rzeczywistości nadmiernie obciążać elementy magazynujące energię, jeśli ładowanie nie jest odpowiednio dostosowane. W niniejszym badaniu oceniono trwałość baterii litowo-jonowych w takich warunkach, wykorzystując zbiory danych NASA PCoE i opracowując zasady ładowania dostosowane do ograniczonego źródła zasilania. Po pierwsze, badamy zachowanie temperatury i napięcia oraz identyfikujemy umiarkowany zakres roboczy, co motywuje do zastosowania podstawowego trybu powolnego ładowania, który zmniejsza obciążenie elektrochemiczne. Po drugie, wprowadzamy dwie warstwy sterowania: dynamiczny, oparty na regułach kontroler, który zmniejsza prąd, gdy napięcie na zaciskach zbliża się do 4,2 V lub temperatura przekracza 24°C w określonym bezpiecznym zakresie, oraz inteligentne kontrolery (Random Forest, XGBoost, Gradient Boosting), które przewidują stopniową degradację na podstawie rutynowo mierzonych sygnałów i wybierają prąd, który minimalizuje oczekiwane uszkodzenia w tych samych warunkach. Wykorzystując liczbę cykli do SoH = 0,7 jako punkt końcowy, sterowanie dynamiczne wydłuża żywotność z 30 do 43 cykli, podczas gdy inteligentne sterowniki osiągają 45, 47 i 48 cykli. Mechanizm ten wspierają zmniejszone szczyty napięcia i tętnienia oraz niższa ekspozycja termiczna. Ogólnie rzecz biorąc, dostosowanie powolnego, uwzględniającego stan i predykcyjnego ładowania do dostępności piezoelektrycznej znacznie wydłuża żywotność i poprawia wydajność pozyskiwania energii, umożliwiając tworzenie bardziej niezawodnych i wymagających mniej konserwacji systemów biomedycznych oraz systemów Internetu rzeczy.
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