Minimum-Fuel Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

Robuschi, Nicolò; Salazar, Mauro; Viscera, Nicola; Braghin, Francesco; Onder, Christopher H.

doi:10.1109/tvt.2020.3030088

Cited by 21 publications

(9 citation statements)

References 39 publications

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“…For reasons of confidentiality, sensitive data have been normalized. We relax constraint (14), substitute (13), and convert it to forces as…”

Section: Transmissionmentioning

confidence: 99%

“…The first line consists of offline control strategies for the energy management of hybrid electric vehicles. In general, such non-causal approaches are based on Pontryagin's Minimum Principle (PMP) [2]- [5], dynamic programming (DP) [6]- [8], and convex optimization [9]- [12], sometimes combining the three methods together [13], [14]. In addition, nonlinear optimization solvers are used for solving the hybrid electric energy management problem, such as IPOPT [15].…”

Section: Introductionmentioning

confidence: 99%

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A Convex Optimization Framework for Minimum Lap Time Design and Control of Electric Race Cars

Borsboom

Fahdzyana

Hofman

et al. 2021

IEEE Trans. Veh. Technol.

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“…For reasons of confidentiality, sensitive data have been normalized. We relax constraint (14), substitute (13), and convert it to forces as…”

Section: Transmissionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Convex Optimization Framework for Minimum Lap Time Design and Control of Electric Race Cars

Borsboom

Fahdzyana

Hofman

et al. 2021

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Earliest of works that used gradient-based optimization for HEV energy management used linear programming [15]. Even recent works involve linear programming such as fast iterative-LP [16] which used one real-valued state variable. The main consideration for LP based problems is to simplify and approximate the model into a linear form, such as the Willan's line approximation for the internal combustion engine.…”

Section: Gradient-based Optimization For Energy Management Inmentioning

confidence: 99%

Comprehensive Energy Footprint Benchmarking Algorithm for Electrified Powertrains

Anwar¹,

Vishwanath²,

Chunodkar³

et al. 2021

Preprint

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Autonomy and electrification in automotive control systems have made modern-day powertrains one of the most complex cyber-physical systems. This paper presents a benchmark algorithm to quantify the performance of complex automotive systems exhibiting mechanical, electrical, and thermal interactions at various timescales. Traditionally Dynamic Programming has been used for benchmarking the performance, however, it fails to deliver results for system with higher number of states and control lever due to curse of dimensionality. We propose "PS3", a three-step algorithm for mixed-integer nonlinear optimal control problems with application to powertrain energy management. PS3 uses pseudo-spectral collocation theory for highly accurate modeling of dynamics. Based on the validated powertrain component models, we have addressed simultaneous optimization of electrical (SOC), vehicular (eco-driving) and thermal (after-treatment and battery temperatures) dynamics along with an integer (gear and engine on/off) control and its corresponding (dwelltime) constraints. PS3 is used to solve such large-scale powertrain problems having fast and slow dynamic states, discontinuous behaviors, non-differentiable and linearly interpolated 1-D and 2-D maps, as well as combinatorial constraints. Five case study powertrain control problems are given to benchmark the accuracy and computational effort against Dynamic Programming. Our analysis shows that this algorithm does not scale computational burden as Dynamic Programming does, and can handle highly complex interactions that occur in modern-day powertrains, without compromising nonlinear and complex plant modeling.

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“…To decrease fuel consumption, drivers should avoid excessive changes in acceleration and deceleration and minimize their frequency of gear changes. (11,(13)(14)(15)(16) On the basis of driving parameters such as car speed and engine rotational speed, Jachimczyk et al categorized different driving styles as ordinary, calm, aggressive, and unnatural. They determined that aggressive and unnatural driving greatly increase both fuel consumption and accident rates.…”

Section: Introductionmentioning

confidence: 99%

Integration of Second-generation On-board Diagnostics Data via Deep Learning to Develop Eco-driving Analysis System Applicable to Large and Small Cars

Chen¹,

Tian²,

Teng³

et al. 2022

Sensors and Materials

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Reducing greenhouse gas emissions is an imperative of climate policy worldwide. The transport sector accounts for a large proportion of CO 2 emissions; therefore, the development of eco-driving has become a critical topic in the study of fuel efficiency and environmental protection. Although considerable research has been carried out on cars, there has been little research involving large vehicles. In this study, second-generation on-board diagnostics (OBD-II) was used to sense and collect the driving data of cars and light-duty buses. These data were then used for predicting real-time fuel consumption by using deep learning methods and a fuel efficiency driving analysis system for both large and small cars. The prediction results demonstrated a correlation coefficient of approximately 90% with actual data and confirmed the applicability of the system to different vehicle types. This system can be integrated with professional driver training centers to improve training quality and promote the development of eco-driving.

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Minimum-Fuel Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

Cited by 21 publications

References 39 publications

A Convex Optimization Framework for Minimum Lap Time Design and Control of Electric Race Cars

A Convex Optimization Framework for Minimum Lap Time Design and Control of Electric Race Cars

Comprehensive Energy Footprint Benchmarking Algorithm for Electrified Powertrains

Integration of Second-generation On-board Diagnostics Data via Deep Learning to Develop Eco-driving Analysis System Applicable to Large and Small Cars

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