CN-121745920-B - New energy vehicle predictive maintenance method and system based on cloud data analysis

Abstract

The application relates to the technical field of vehicle maintenance, in particular to a new energy vehicle predictive maintenance method and system based on cloud data analysis. The method comprises the steps of obtaining a vehicle running dynamic information set, analyzing space-time sequence association of brake disc degradation and bus line characteristics based on the vehicle running dynamic information set to obtain predictive maintenance guide information, tracking vehicle running behavior data based on the predictive maintenance guide information, calculating a real-time accumulated load spectrum of a bus line, triggering vehicle personalized maintenance behavior to obtain a vehicle maintenance information set, dynamically adjusting a bus maintenance control strategy based on the vehicle maintenance information set, and outputting a new energy vehicle predictive maintenance log. The maintenance strategy is dynamically adjusted to adapt to the actual state of the vehicle, so that the interruption of operation of the new energy bus caused by faults is reduced, and the intelligent level and comprehensive operation benefit of the new energy bus maintenance are improved.

Inventors

• LAN TIANFU
• ZHU HONG
• WANG YU

Assignees

• 厦门磁北科技有限公司

Dates

Publication Date

20260512

Application Date

20260226

Claims (6)

1. 1. The new energy vehicle predictive maintenance method based on cloud data analysis is characterized by comprising the following steps of: Acquiring a vehicle running dynamic information set, and analyzing the space-time sequence association of brake disc degradation and bus line characteristics based on the vehicle running dynamic information set to obtain predictive maintenance guide information; The vehicle running dynamic information set uses a vehicle-mounted sensor carried by the new energy bus as a data source; Based on the predictive maintenance guide information, tracking vehicle running behavior data, calculating a real-time accumulated load spectrum of a bus line, triggering the personalized maintenance behavior of the vehicle, and obtaining a vehicle maintenance information set; The analyzing the space-time sequence association of the brake disc degradation and the bus line characteristic based on the vehicle running dynamic information set to obtain predictive maintenance guide information comprises the following steps: the vehicle running dynamic information set comprises vehicle historical braking data, line geographic information and a line running schedule; Based on the line geographic information, analyzing the differential influence of the space characteristics of each road section in the bus operation line on the heat load of the brake disc to obtain the space degradation information of the brake disc; based on the historical braking data of the vehicle, analyzing the time sequence change rule of the braking behavior of the vehicle in different operation time periods of the same line by combining the line operation time schedule to obtain time sequence degradation information of a brake disc; Integrating the brake disc space degradation information and the brake disc time sequence degradation information, and constructing a space-time sequence association rule reflecting the coupling of line characteristics and brake disc degradation to obtain the predictive maintenance guide information; the construction process of the space-time sequence association rule comprises the following steps: based on the space degradation information of the brake disc, extracting degradation performance of a key road section of the brake heat load in early peak, late peak, flat peak and night low peak by combining the time sequence degradation information of the brake disc, and obtaining a key road section-time period degradation characteristic set with a space-time tag; Based on the key road section-time period degradation characteristic set, identifying coupling phenomena that degradation contribution of different space road sections is amplified or suppressed in different time periods, and obtaining a joint modulation relation of the space characteristic and the time period characteristic to degradation rate; based on the joint modulation relation, carrying out dynamic association mapping on the line space degradation characteristic and the degradation time sequence curve to generate a compound association degree for quantifying the degradation risk intensity of the brake disc at any running time and any line position, and taking calculation and matching logic of the compound association degree as the space-time sequence association rule; The line space degradation characteristic and the degradation timing curve are in bidirectional coupling positive correlation; the construction process of the vehicle maintenance information set comprises the following steps: According to the space-time sequence association rule, vehicle position data and time data which are acquired in real time are matched with the composite association degree corresponding to the current running time and the bus line position, and the real-time composite association degree is obtained; Based on the real-time compound association degree, analyzing the comprehensive degradation state of the current brake disc formed by the combined action of the real-time operation load and the historical accumulated degradation information to obtain a real-time accumulated load spectrum; based on the real-time accumulated load spectrum, triggering maintenance early warning and executing instructions according to the current comprehensive degradation state, and generating maintenance levels, recommended maintenance time windows and maintenance operation items aiming at corresponding buses to obtain the vehicle maintenance information set; the construction process of the real-time accumulated load spectrum comprises the following steps: Based on the real-time compound association degree, analyzing the real-time operation load intensity level corresponding to the current operation time and the bus line position to obtain a real-time load level; based on the historical accumulated degradation information, analyzing an instant load superposition effect and a dynamic acceleration effect generated by the real-time load grade on the historical accumulated degradation information to obtain a real-time load action characteristic; And dynamically correcting the historical accumulated degradation information based on the real-time load action characteristic to generate a comprehensive degradation state evaluation value which integrates the current real-time operation load influence and the historical degradation accumulation result, and continuously updating a set of influences on the brake disc along with the operation of the bus by using the comprehensive degradation state evaluation value as the real-time accumulated load spectrum.
2. 2. The method according to claim 1, wherein the construction process of the brake disc space degradation information comprises: identifying and extracting long downhill road sections, continuous curve road sections and platform dense area road sections in the line based on the line geographic information, and taking the long downhill road sections, the continuous curve road sections and the platform dense area road sections as brake heat load key road sections; Based on the braking heat load key road sections, analyzing a space synergistic effect and a heat accumulation effect of braking behaviors formed between adjacent key road sections to obtain inter-road section braking load related information; and analyzing line space degradation characteristics reflecting the difference of contribution of different space positions to the thermal load of the brake disc based on the brake thermal load key road sections and the inter-road section brake load related information to obtain the brake disc space degradation information.
3. 3. The method according to claim 2, wherein the construction process of the brake disc timing degradation information includes: analyzing the distribution characteristics of the bus braking frequency and the braking intensity in each period of the early peak, the late peak, the flat peak period and the night low peak period based on the line operation schedule and combining the historical braking data of the vehicle to obtain time-period braking information; based on the time-lapse braking information, analyzing the differential influence of frequent start and stop in peak time and high-strength braking, medium-strength braking in steady operation in peak time and low-strength braking in low peak time on the thermal fatigue accumulation of the brake disc respectively to obtain time-lapse braking load characteristics; And analyzing a degradation time sequence curve reflecting the degradation rate of the brake disc along with the change of a time wave band based on the time-lapse brake information and the time-lapse brake load characteristics to obtain the time-lapse brake disc degradation information.
4. 4. A method according to claim 3, wherein said analyzing reflects a degradation timing profile of a brake disc degradation rate over a time band, comprising: analyzing a reference level of the degradation rate of the brake disc in each period of the early peak, the late peak, the flat peak period and the night low peak period based on the time sequence braking load characteristics, and obtaining a degradation rate period reference spectrum according to a step change rule of the reference level during switching along with the operation period; Analyzing the inheritance effect of the thermal fatigue accumulated in the previous operation period on the initial degradation state of the next period and the dynamic modulation effect of the inheritance effect on the degradation rate of the next period by combining the period braking information based on the degradation rate period reference spectrum to obtain a period degradation rate modulation relation; And constructing the degradation time sequence curve which completely reflects continuous and self-adaptive change of the degradation rate of the brake disc in different time bands according to the degradation rate time period reference spectrum and the inter-time degradation rate modulation relation.
5. 5. The method of claim 4, wherein dynamically adjusting a bus maintenance control strategy based on the vehicle maintenance information set, outputting a new energy vehicle predictive maintenance log, comprises: Based on the vehicle maintenance information set, analyzing the aggregation trend and the collaborative maintenance time of the maintenance demands among a plurality of vehicles according to the real-time accumulated load spectrums of different vehicles in different lines to obtain a cluster maintenance scheduling strategy; Based on the cluster maintenance scheduling strategy, dynamically optimizing and combining the suggested maintenance time windows of each vehicle to generate a collaborative maintenance execution sequence considering the degradation emergency degree of the bicycle and the overall maintenance resource efficiency; And scheduling and executing the specific maintenance operation item according to the collaborative maintenance execution sequence, automatically recording actual load characteristics, maintenance measures and effect verification data in the maintenance process, and integrating the actual load characteristics, the maintenance measures and the effect verification data into a cloud database to form a structured predictive maintenance log of the new energy vehicle.
6. 6. The new energy vehicle predictive maintenance system based on cloud data analysis, which is applied to the method as claimed in any one of claims 1 to 5, and comprises the following steps: the maintenance guide analysis module is used for acquiring a vehicle running dynamic information set, and analyzing the space-time sequence association of the brake disc degradation and the bus line characteristics based on the vehicle running dynamic information set to obtain predictive maintenance guide information; The accumulated load analysis module is used for tracking vehicle running behavior data based on the predictive maintenance guide information, calculating a real-time accumulated load spectrum of a bus line, triggering the personalized maintenance behavior of the vehicle and obtaining a vehicle maintenance information set; And the maintenance control analysis module is used for dynamically adjusting the bus maintenance control strategy based on the vehicle maintenance information set and outputting a new energy vehicle predictive maintenance log.

Description

New energy vehicle predictive maintenance method and system based on cloud data analysis Technical Field The application relates to the technical field of vehicle maintenance, in particular to a new energy vehicle predictive maintenance method and system based on cloud data analysis. Background At present, the maintenance of new energy buses mainly adopts a real-time alarm mechanism based on regular planned maintenance and a simple threshold value, and the existing scheme usually carries out health evaluation and alarm based on whole data of a fleet or a general model and cannot be deeply combined with the dynamic operation characteristics of a specific bus line. However, under the actual scene that a bus line is fixed but road conditions and passenger flows are complex and changeable, the method has systematic limitations that a targeted degradation model cannot be built according to the unique gradient, congestion and passenger flow changes of the line, multisource dynamic data fusion is insufficient, regularity and sudden load are difficult to accurately describe and influence on component fatigue accumulation exists, a maintenance strategy is lack of self-adaptive adjustment capability based on line clustering and real-time environment, early warning lag and low maintenance efficiency are caused, and currently, in the field of new energy bus operation and maintenance, accurate prediction and personalized maintenance of thermal fatigue and cracks of a brake disc are carried out by means of cloud data analysis and fusion of real-time multidimensional data, and a complete technical system is still lacked. Disclosure of Invention The application provides a new energy vehicle predictive maintenance method and system based on cloud data analysis, which are used for solving the problems. The application provides a new energy vehicle predictive maintenance method based on cloud data analysis, which comprises the steps of obtaining a vehicle running dynamic information set, analyzing space-time sequence association of brake disc degradation and bus line characteristics based on the vehicle running dynamic information set to obtain predictive maintenance guide information, tracking vehicle running behavior data based on the predictive maintenance guide information, calculating a real-time accumulated load spectrum of a bus line, triggering vehicle personalized maintenance behavior to obtain a vehicle maintenance information set, dynamically adjusting a bus maintenance control strategy based on the vehicle maintenance information set, and outputting a new energy vehicle predictive maintenance log. According to the technical scheme, the predictive maintenance method establishes space-time sequence association of brake disc degradation and line characteristics by means of cloud data analysis, achieves accurate pre-judgment of maintenance requirements, avoids excessive maintenance and insufficient maintenance, reduces operation cost by means of personalized maintenance behavior, dynamically adjusts maintenance strategies to adapt to actual states of vehicles, prolongs service lives of key parts, guarantees driving safety, reduces operation interruption caused by faults, and improves intelligent maintenance level and comprehensive operation benefit of new energy buses. The method comprises the steps of analyzing space-time sequence association of brake disc degradation and bus line characteristics based on a vehicle running dynamic information set to obtain predictive maintenance guide information, analyzing the differential influence of the space characteristics of each road section in a bus running line on brake disc heat load based on the line geographic information to obtain brake disc space degradation information, analyzing the time sequence change rule of brake behaviors of a vehicle in different running periods of the same line based on the vehicle historical brake data in combination with the line running time table to obtain brake disc time sequence degradation information, integrating the brake disc space degradation information and the brake disc time sequence degradation information, and constructing space-time sequence association rules reflecting coupling of the line characteristics and the brake disc degradation to obtain the predictive maintenance guide information. The construction process of the brake disc space degradation information comprises the steps of identifying and extracting long downhill road sections, continuous curve road sections and platform dense area road sections in a line based on the line geographic information to serve as brake heat load key road sections, analyzing space synergistic effect and heat accumulation effect of braking actions formed between adjacent key road sections based on the brake heat load key road sections to obtain inter-road brake load related information, and analyzing line space degradation characteristics reflecting difference of contribution of different