Method for optimizing passenger comfort in a railway vehicle

Patent metadata

Field	Value
Publication number	US-10259473-B2
Application number	US-201715427756-A
Country	US
Kind code	B2
Filing date	Feb 8, 2017
Priority date	Feb 9, 2016
Publication date	Apr 16, 2019
Grant date	Apr 16, 2019

Abstract

The vehicle including an active suspension system ( 22 ) parameterized by a set of adjustment parameters. The railway track is cut into segments. For each segment (T), the method includes campaigns for optimization of the set of parameters, such that: during the first campaign, to each passage of the suspension system ( 22 ) on the segment (T), a first set of parameters, specific to this passage, is predefined and applied to the suspension system ( 22 ), and a comfort quality index is calculated, and then a metaheuristic algorithm is applied for determining second sets of parameters, and during each following optimization campaign, at each passage of the suspension system over the segment, one of the determined sets of parameters by the previous optimization campaign is applied to the suspension system, and the comfort quality index is calculated, and then the metaheuristic algorithm is applied in order to determine new sets of parameters.

First claim

The invention claimed is:

1. A comfort optimization method for optimizing comfort in a railway vehicle having multiple railway cars circulating on a railway track, the railway vehicle including at least one active suspension system parameterized by a set of adjustment parameters, wherein:
the railway track is cut out in segments, and
for each segment, the method comprises optimization campaigns for optimization of the set of adjustment parameters, such that:
first sets of adjustment parameters are predefined,
during a first optimization campaign, at each passage of the active suspension system on the segment, one of the first sets of adjustment parameters, specific to this passage, is applied to the active suspension system, and a comfort quality index is calculated, and then a metaheuristic algorithm is applied for determining second sets of adjustment parameters,
during each following optimization campaign, at each passage of the active suspension system over the segment, one of the sets of adjustment parameters determined by the previous optimization campaign is applied to the suspension system and the comfort quality index is calculated, and then the metaheuristic algorithm is applied for determining new sets of adjustment parameters;

wherein the optimization campaigns are common to a plurality of railway vehicles of a fleet of independent railway vehicles circulating on the same railway track, the railway vehicles then being able to put the data relative to each optimization campaign into a common database so that the information may be shared with any railway vehicle in the plurality of railway vehicles of the fleet.

2. The comfort optimization method according to claim 1 , wherein the adjustment parameters of the first sets of adjustment parameters are selected in predetermined respective safety intervals.

3. The comfort optimization method according to claim 1 , wherein the adjustment parameters of the first sets of adjustment parameters are determined randomly.

4. The comfort optimization method according to claim 3 , wherein the metaheuristic algorithm is a genetic algorithm.

5. The comfort optimization method according to claim 1 , wherein the metaheuristic algorithm is a genetic algorithm.

6. The comfort optimization method according to claim 1 , wherein the cutting out of the railway track in segments is adaptive, with segments of variable lengths, established according to singularities of the railway track.

7. A comfort control device in a railway vehicle having multiple railway cars, for applying a comfort optimization method, comprising:
at least one active suspension system, adjustable based on a set of adjustment parameters,
a system for controlling the set of adjustment parameters of the active suspension system,
a software device for evaluating passenger comfort in the railway vehicle, and
an optimization device including a database able to determine optimized sets of adjustment parameters by carrying out a plurality of optimization campaigns as follows:
on a railway track cut out in segments, for each segment, the optimization campaigns are such that:
during a first optimization campaign, at each passage of the active suspension system on the segment, a first set of adjustment parameters, specific to this passage, is predefined and applied to the active suspension system, and a comfort quality index is calculated, and then a metaheuristic algorithm is applied for determining second sets of adjustment parameters,
during each following optimization campaign, at each passage of the active suspension system over the segment, one of the sets of adjustment parameters determined by the previous optimization campaign is applied to the suspension system and the comfort quality index is calculated, and then the metaheuristic algorithm is applied for determining new sets of adjustment parameters;

wherein the optimization campaigns are common to a plurality of railway vehicles of a fleet of independent railway vehicles circulating on the same railway track, the railway vehicles then being able to put the data relative to each optimization campaign into a common database so that the information may be shared with any railway vehicle in the plurality of railway vehicles of the fleet.

8. The comfort control device according to claim 7 , wherein the optimization device is able to be updated at each optimization campaign, indicating the set of adjustment parameters according to track segments, speeds and loads of the vehicle.

9. The comfort control device according to claim 7 , wherein the optimization device include a generation device for generating optimized sets of adjustment parameters by the metaheuristic algorithm, able to generate a set of optimized adjustment parameters by applying the metaheuristic algorithm to the set of adjustment parameters determined during a previous optimization campaign.

Assignees

• Alstom Transp Tech

Inventors

• Hallonet Frédéric

Classifications

• B60G2300/102

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• B60G2300/10

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• B61F5/38

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• B61L23/042

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• B61F5/245 Primary

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