English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Increasing the fuel economy of hybrid electric vehicles (HEVs) and extendedrange electric vehicles (EREVs) through optimization-based energy managementstrategies (EMS) has been an active research area in transportation. However,it is difficult to apply optimization-based EMS to current in-use EREVs becauseinsufficient knowledge is known about future trips, and because such methodsare computationally expensive for large-scale deployment. As a result, mostpast research has been validated on standard driving cycles or on recordedhigh-resolution data from past real driving cycles. This paper improves anin-use rule-based EMS that is used in a delivery vehicle fleet equipped withtwo-way vehicle-to-cloud connectivity. A physics model-guided online Bayesianframework is described and validated on large number of in-use driving samplesof EREVs used for last-mile package delivery. The framework includes: adatabase, a preprocessing module, a vehicle model and an online Bayesianalgorithm module. It uses historical 0.2 Hz resolution trip data as input andoutputs an updated parameter to the engine control logic on the vehicle toreduce fuel consumption on the next trip. The key contribution of this work isa framework that provides an immediate solution for fuel use reduction ofin-use EREVs. The framework was also demonstrated on real-world EREVs deliveryvehicles operating on actual routes. The results show an average of 12.8% fueluse reduction among tested vehicles for 155 real delivery trips. The presentedframework is extendable to other EREV applications including passengervehicles, transit buses, and other vocational vehicles whose trips are similarday-to-day.

A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles