Noodoe is a world chief in EV charging innovation, providing superior options that empower operators to optimize their charging station operations and supply distinctive consumer experiences. Their common charging stations are suitable with all EV manufacturers and have intuitive fee choices, together with bank cards and Apple Pay. Powered by the Noodoe EV OS cloud administration system, the corporate delivers round the clock automated monitoring, diagnostics, and upkeep, attaining a market-leading uptime of 99.83%. With operations in over 15 international locations and a sturdy dedication to sustainability, Noodoe is reworking the EV charging trade by means of cutting-edge know-how and a user-first method.
Regardless of its technological strengths, Noodoe has encountered key challenges in serving to station operators optimize efficiency and choose probably the most cost-effective electrical energy pricing methods throughout numerous markets. Conventional methods lack the aptitude to effectively course of huge quantities of real-time and historic knowledge or present personalised, station-level suggestions. This limits operators’ means to make well timed, knowledgeable selections—leading to larger electrical energy prices, underutilized belongings, and a subpar buyer expertise. These inefficiencies not solely cut back profitability but additionally hinder the flexibility to scale in a aggressive and fast-evolving EV charging panorama.
To unravel this, Noodoe has built-in massive language fashions (LLMs) by means of Amazon Bedrock and Amazon Bedrock Brokers to ship clever automation, real-time knowledge entry, and multilingual assist. These AI-powered instruments analyze utilization patterns, station diagnostics, and exterior variables like climate or grid situations to generate extremely tailor-made pricing suggestions. By utilizing the structured orchestration and prompt-based reasoning of Amazon Bedrock, Noodoe equips operators with actionable insights that enhance margins, improve station utilization, and permit them to supply extra aggressive charges to customers—in the end boosting buyer satisfaction. This service is delivered by means of a subscription mannequin, creating a brand new, scalable income stream for Noodoe whereas reinforcing its management and innovation within the EV infrastructure area.
On this put up, we discover how Noodoe makes use of AI and Amazon Bedrock to optimize EV charging operations. By integrating LLMs, Noodoe enhances station diagnostics, allows dynamic pricing, and delivers multilingual assist. These improvements cut back downtime, maximize effectivity, and enhance sustainability. Learn on to find how AI is reworking EV charging administration.
Answer overview
The Noodoe AI-enhanced diagnostics stream is constructed on a multi-step course of that mixes knowledge assortment, AI-powered analytics, and seamless translation for international accessibility, as illustrated within the following determine.
The bodily charging station community presently operates over 1,000 websites throughout greater than 20 international locations, with plans to increase by greater than 50 further websites by the top of 2025. As illustrated within the following picture, it makes use of the EV Cloud and LLMs to generate related suggestions following backend processing.
The next screenshot exhibits an instance of the ends in the UI.
Overview of Noodoe AI-enhanced diagnostics
The next diagram illustrates the answer knowledge stream.
To satisfy the characteristic necessities, the system operation course of contains the next steps:
- Charging knowledge is processed by means of the EV service earlier than coming into the database.
- The charging historical past knowledge and pricing knowledge are saved within the EV database.
- Amazon EventBridge Scheduler periodically triggers the EV service to carry out evaluation.
- The EV service calls the AI service to investigate historic knowledge and supply pricing suggestions.
- The AI service collects the organized historic knowledge to organize the immediate template.
- This info, mixed with applicable prompts, is used along with Amazon Bedrock Brokers as an AI-pricing agent to extract related info. The AI-pricing agent analyzes this mixed knowledge to establish every day peak and off-peak durations and supply suggestions for consumer pricing plans.
- Optionally, if translation is required for non-English customers, these outcomes from the AI-pricing agent are additional processed by means of one other Amazon Bedrock agent for translation.
- Optionally, the interpretation agent makes use of Anthropic’s Claude Sonnet 3.5 on Amazon Bedrock to get the outcome within the corresponding language.
- Lastly, the AI service collects the ends in the consumer’s language for formatting and different processing, then inserts them right into a template to create a complete report that’s pushed to the consumer’s finish.
Within the following part, we dive deep into these steps and the AWS providers used.
Structure of Noodoe AI-enhanced diagnostics
Noodoe confronted key challenges in constructing a globally scalable, dependable, and cost-efficient structure. They wanted an answer that would assist fast growth, deal with excessive knowledge volumes, and ship constant efficiency throughout AWS Areas. Addressing these necessities required cautious architectural planning to offer flexibility and resilience.
The next diagram illustrates the answer structure Noodoe constructed to beat these challenges to assist international progress.
The EV charging optimization platform constructions the information stream throughout a number of AWS providers, offering environment friendly knowledge ingestion, processing, and AI-driven decision-making. Amazon Elastic Kubernetes Service (Amazon EKS) retrieves knowledge from Amazon DocumentDB, processes it, and invokes Amazon Bedrock Brokers for reasoning and evaluation. This structured knowledge pipeline allows optimized pricing methods and multilingual buyer interactions. By utilizing containerized purposes, event-driven workflows, and AI capabilities, the system supplies scalable and versatile insights to EV station operators.
Knowledge ingestion and processing
EV charging stations ship real-time charging knowledge to AWS IoT Core, which acts because the preliminary entry level for knowledge processing. The info is then transmitted to Amazon Managed Streaming for Apache Kafka (Amazon MSK) to facilitate high-throughput, dependable streaming. From Amazon MSK, knowledge flows into Amazon EKS, the place the EV service processes it earlier than storing the charging historical past and development information in DocumentDB. This structured storage supplies environment friendly retrieval for evaluation and prediction.
AI-powered pricing evaluation
To optimize pricing methods, Amazon EventBridge triggers a pricing prediction perform at common intervals. This perform retrieves historic charging knowledge from DocumentDB and sends it, together with predefined prompts, to the Amazon Bedrock AI-pricing agent. The AI agent, powered by Anthropic’s Claude on Amazon Bedrock, evaluates station utilization developments, peak and off-peak durations, and pricing inefficiencies to generate optimum pricing suggestions. Though the pricing agent doesn’t entry an Amazon Bedrock information base or set off motion teams, it makes use of preprocessing and put up processing options to refine predictions and enhance decision-making.
Multilingual assist and report era
If translation is required, the pricing evaluation outcomes are forwarded to the Amazon Bedrock translate agent, which converts the insights into the operator’s most popular language. The translated and structured knowledge is then formatted right into a predefined report template and saved in a chosen database for later retrieval. This supplies seamless entry to actionable insights throughout numerous markets.
UI, monitoring, and efficiency optimization
Operators entry the system by means of a web-based UI, with Amazon Route 53 and Amazon CloudFront offering quick and environment friendly content material supply. An Software Load Balancer distributes incoming requests throughout a number of EKS situations, offering excessive availability. To optimize efficiency, Amazon ElastiCache accelerates knowledge retrieval whereas lowering database load. For system monitoring and observability, Amazon CloudWatch supplies further monitoring and observability. The administrator of Noodoe makes use of Amazon Managed Service for Prometheus and Amazon Managed Grafana for system monitoring and visualization.
This structure empowers Noodoe with an AI-driven, scalable, and clever EV charging administration answer, enhancing station utilization, income optimization, and buyer expertise worldwide.
Abstract
The Noodoe AI-enhanced diagnostics stream transforms EV charging operations by integrating Amazon Bedrock Brokers, mixing rule-based automation, real-time consumer enter, and LLM-powered insights for smarter decision-making. Backed by a complete information base and streamlined APIs, the answer empowers operators to automate workflows, optimize pricing, and increase station efficiency at scale. Ongoing growth of the information base, workflow refinement, and real-world testing additional enhance effectivity and reliability. This method has delivered a 15% enhance in income and lowered implementation time by 10%. Steady suggestions and clear documentation equip customers to successfully use AI-driven diagnostics for extra clever charging administration.
Roman Kleinerman, Vice President of Merchandise at Noodoe, shares: “We’ve seen income will increase of 10–25% relying on the situation and variety of stations, as prospects use our Al answer to optimize pricing methods.”
Noodoe is devoted to delivering smarter, extra clever EV charging providers that profit each end-users and operators. Presently, Noodoe operates over 1,000 charging websites throughout greater than 20 international locations, with plans to increase by greater than 50 further websites by the top of 2025. Wanting forward, the system is being enhanced to assist close to real-time dynamic pricing optimization by incorporating components comparable to demand, grid situations, time of day, and climate. Amazon Bedrock Brokers assist allow these clever capabilities, powering dynamic pricing, load balancing, and grid-aware routing to optimize power distribution and information customers to probably the most environment friendly stations. Future enhancements will provide personalised charging suggestions and incentives based mostly on consumer preferences, maximizing worth for each prospects and operators. Begin constructing clever, AI-powered EV charging options with Amazon Bedrock.
Concerning the Authors
Ray Wang is a Senior Options Architect at AWS. With 12 years of expertise within the IT trade, Ray is devoted to constructing fashionable options on the cloud, particularly in NoSQL, massive knowledge, machine studying, and Generative AI. As a hungry go-getter, he handed all 14 AWS certificates to make his technical area not solely deep however large. He likes to learn and watch sci-fi films in his spare time.
Howard Su is a Options Architect at AWS. With a few years of expertise in software program growth and system operations, Howard have served in varied roles together with RD, QA, and SRE, and Howard have been chargeable for the architectural design of quite a few large-scale methods, in addition to collaborating in a number of cloud migrations. After accumulating years of growth and operations expertise, Howard is devoted to selling cloud-native service applied sciences and changing into an advocate for DevOps.
Tony Trinh is a Senior AIML Specialist Architect at AWS. With 13+ years of expertise within the IT trade, Tony makes a speciality of architecting scalable, compliance-driven AI and ML options—notably in generative AI, MLOps, and cloud-native knowledge platforms. As a perpetual learner, he’s doing analysis in Visible Language Mannequin, Accountable AI & Pc Imaginative and prescient and authoring a guide in ML engineering. In his spare time, Tony enjoys out of doors exercise, experimenting with house enchancment, and exploring Melbourne’s vibrant espresso scene.
