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Smart busses: hybrid vehicle drive switching optimization

IP.com Disclosure Number: IPCOM000249243D
Publication Date: 2017-Feb-14
Document File: 2 page(s) / 23K

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Smart busses: hybrid vehicle drive switching optimization

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Smart busses : hybrid vehicle drive switching optimization

Hybrid vehicles - especially those used for public transportation in some circumstances start combustion engine/switch drive from electrical in incorrect moments of it's travel. For example a bus stops at bus stop and diesel engine is turned off, all passengers get of/get in the bus and bus starts propelled by electric motor. Then, triggered by some "on board" logic (reaching certain speed, based on some logic related to energy stored in battery etc .), a diesel is started and is propelling the bus - but there is an intersection with long-lasting red light ahead, where the bus needs to stop again and wait . And possibly stop-start diesel engine again. This is bad for at least few reasons: - starting and stopping combustion engine is energy costly (electrical for the starter, fuel as just started engine consumes more for some time ) - stated above this is very environmentally unfriendly (more than typical toxic exhaust gas production during engine start , noise pollution) - it generates unnecessary mechanical stress and wear for the whole vehicle , especially engine and it 's "orchestration"

In simple words we propose to change mechanisms of taking decision about drive mode switching to include external and/or historical data (by external we mean external to powertrain and vehicle mechanisms; not necessarily coming from "outside" of the vehicle).

Idea works as extension/replacement of existing means of electric /combustion drive switching. Many vehicles use very simple algorithms for drive switching - such as based on battery level or speed attained (use electric motor till certain vehicle speed). Using more data for decision taking is very applicable (and easy/efficient) to do for mass-transportation vehicles that have fixed routes . But is also applicable to consumer vehicles. By combining external data like:

geographic (position, terrain profile),  traffic info (congestion info),  possible obstacles (traffic lights, level crossings etc.) 

and historical data about the route  about driver behavior

one can optimize...