Battery temperature aware equivalent consumption minimization strategy for mild hybrid electric vehicle powertrains

Acquarone, Matteo; Anselma, Pier Giuseppe; Miretti, Federico; Misul, Daniela Anna

doi:10.1109/vppc55846.2022.10003225

Cited by 1 publication

(3 citation statements)

References 19 publications

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“…In this subsection, the Th-ECMS and its offline solution method are introduced [29]. The offline solution method is required to obtain fuel-optimal trajectories that satisfy the battery temperature constraint for a given driving cycle.…”

Section: Th-ecms and Offline Solutionmentioning

confidence: 99%

“…This lack of smoothness is ultimately caused by the presence of non-continuously differentiable functions such as linear interpolants 4 in the vehicle model. It should be noted that, although the PSO algorithm is able to reduce the objective function (29) to a very small quantity, it is never able to reduce it to exactly zero. Among other aspects, this is due to the many approximations in the optimization process, such as the numerical integration error and the approximation of the adjoint equations by finite differences.…”

Section: Indirect Shooting Based On Particle Swarm Optimizationmentioning

confidence: 99%

“…In this work, an offline and online version of the Thermal ECMS (Th-ECMS) are developed to minimize fuel consumption, guaranteeing that battery overheating is avoided. The offline version of the Th-ECMS, developed in [29], is based on Pontriyagin's Minimum Principle to obtain the optimal solution, exploiting a priori knowledge of the driving cycle. The optimal EFs values, obtained by the offline Th-ECMS algorithm over a set of driving cycles, are stored and used to train an NN which estimates the optimal EFs as a function of SOC, the battery temperature, and the distance yet to travel.…”

Section: Introductionmentioning

confidence: 99%

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Online Temperature-Aware Equivalent Consumption Minimization Strategy for Mild Hybrid Electric Powertrains

Acquarone,

Miretti,

Anselma

et al. 2024

IEEE Trans. Veh. Technol.

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In this work, an online energy management strategy for mild hybrid electric vehicles is developed to minimize the fuel consumption while simultaneously preventing battery overheating. Since mild hybrids are typically equipped with a passively cooled battery pack, the energy management strategy design needs to keep the battery temperature below an upper limit, preventing accelerated aging and thermal runaway. To address this issue, the equivalent consumption minimization strategy (ECMS) approach is extended to develop a real-time capable controller, termed thermal ECMS (Th-ECMS), that is sensitive to the thermal dynamics of the battery and that can enforce constraints on its temperature. The rationale for our formulation is based on Pontryagin's minimum principle from optimal control theory. The online Th-ECMS is developed on the basis of the offline version of Th-ECMS, introduced in a previous work. Exploiting the a priori knowledge of the driving mission, the offline Th-ECMS calibrates the equivalence factors and obtains the optimal solution, which is compared with the globally optimal dynamic programming solution. This offline calibration method is run on a large number of driving missions and the collected data is used to train a feed-forward neural network that estimates optimal equivalence factors as functions of the battery state of charge, battery temperature, and distance yet to travel. The trained network is then used to populate two lookup tables mapping the equivalence factors, and implementable on the vehicle electronic control unit. Finally, the online Th-ECMS obtains the equivalence factors through the look-up tables in realtime. The online strategy was tested in four different driving missions, achieving a fuel economy remarkably similar to the optimal solution and successfully avoiding battery overheating.

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Section: Th-ecms and Offline Solutionmentioning

confidence: 99%

Section: Indirect Shooting Based On Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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