View source for EV charging - energy and asset management

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Electric Vehicle charging (EV charging) is a '''high power load''' (for example, up to 22kW for a charging station in [[Electric_Vehicle_and_EV_charging_fundamentals#Electric_vehicle_charging_modes|mode 3]]). It is also a '''controllable and shiftable load'''.

This is why [[#EV charging Energy Management|EV charging Energy Management]] is a must, and plays an essential role, on demand side to optimize energy cost and usage, and on grid side to contribute to the grid balance.

Also, with the [[Electric Vehicle trends|booming of EV adoption]], the '''availability of EV charging points''' becomes essential for EV drivers satisfaction.

This makes [[#EVSE Asset management|EVSE asset management]] (Electric Vehicle Supply Equipment) a must for charge point operators to optimize the usage and profitability of their EV charging infrastructure.

==EV charging Energy Management==

EV charging is a new kind of electric load, with unique characteristics in terms of where and when it is used, but also in terms of variable power and energy demands. It is worth noting that its [[EV charging - electrical architectures|connection to new or existing electrical installations]] may have a significant impact on the overall power distribution system.

Power management of EV charging stations is therefore essential, in order to minimize the impact on the existing or new electrical infrastructure while distributing available energy between all connected loads.

There are 3 main levels of power management, depending on the set objective:

*[[#EV charging with STATIC Load Management|Static Load Management]]: limits the power drawn by the EV charging loads to a fixed power level
*[[#EV charging with DYNAMIC Load Management|Dynamic Load Management]]: optimizes energy use at building level and allocates available building power to the EV charging loads
*[[#EV charging - Artificial Intelligence-based Load Management|Artificial Intelligence-based Load Management]]: optimizes energy use and costs based on EV planning, energy tariffs, local production and energy consumption forecasts

These power management levels are detailed in this section. You can also check this [[EV charging - electrical architectures#Example of architecture with mode 3 charging stations according to different load management strategies|example with 3 EV infrastructure scenarios]]: it is the same application, implemented without Load Management System (LMS), with static LMS and with dynamic LMS, to illustrate how it can impact the electrical installation sizing.

EV charging management may also be part of a larger eco-system, such as integration into a [[#Integration of EV charging into a Building Management System (BMS)|next-generation Building Management System (BMS)]], contributor to [[#Demand Response - Peak Shaving applied to EV charging|Demand Response for Smart Grid]] ...

{{FigImage|DB431448_EN|svg|EV45|The different levels of EV charging power management}}

===EV charging with STATIC Load Management===

In static mode, the Load Management System regulates and distributes energy evenly and in real time between all connected vehicles, so as not to exceed the general STATIC setpoint for the vehicle loads.

'''Example''': In a building, a static setpoint of 100 kVA is defined as the power available for the EV chargers, and there is a requirement to install 10x 22 kVA charging points. With the energy management system, regardless of the number of terminals (EV chargers) that are being used simultaneously, it ensures that the 100kVA limit is never exceeded, and that any risk of tripping is avoided.

The current setpoint for each of the charging points is transmitted in real time to the electric cars, which have 5 seconds to apply it. If this instruction is not applied by the car, then the charge point contactor will be instructed to open the circuit.

This allocation method allows you to:

*Evenly distribute available energy between all vehicles that are being charged
*Sequence loads between the vehicles that are connected simultaneously
*Optimize occupant comfort by ensuring that the main power supply does not trip as a result of an influx of vehicles requiring a recharge
*Reduce cost and dimensions for electrical panel dedicated to the power supply of the electric vehicle charging network (100 kVA in this example)

{{FigImage|DB431449_EN|svg|EV46|EV load management with static setpoint}}

===EV charging with DYNAMIC Load Management===

In dynamic mode, the Load Management System allocates available onsite energy in real time to the electric vehicle charging network. In doing so, it also temporarily limits the charging power, to meet the energy constraints imposed by the rest of the electrical installation. Conversely, the power allocated may be higher at times when energy consumption for the rest of the electrical installation is low.

'''Example''': The total power of the building is 250 kVA and the objective is to install 10x 22kVA charging points. With this system, whatever the load of the building and number of terminals (EV chargers) used at the same time, total consumption must never exceed 250kVA by instructing the terminals to adapt in real time to the other loads of the building.

The current setpoint for each charger is transmitted in real time to the cars, which have 5 seconds to apply it. If this instruction is not applied by the car, then the charge point contactor will be instructed to open.

This allocation method allows you to:

*Evenly distribute available energy between all vehicles being charged
*Sequence the loads between the connected vehicles simultaneously
*Optimize occupant comfort by ensuring that the main power supply does not trip as a result of an influx of vehicles requiring a recharge
*Control energy costs by subscribing the optimal energy contract from the energy supplier (may not be applicable in countries where the energy contract has no limit)

To determine, in real time, the DYNAMIC setpoint allocated to the charging infrastructure, the system must measure the available power at building level.

{{FigImage|DB431450_EN|svg|EV47|EV load management with dynamic setpoint}}

===Dynamic load management with additional power from local production===

In case of presence of renewable energy system in the building, the Load Management system can also measure this local production and take it into consideration as an additional available power for the charging stations.

{{FigImage|DB431454_EN|svg|EV48|EV load management with dynamic setpoint including the additional power from Local Production}}

===Demand Response - Peak Shaving applied to EV charging===

Distribution Network Operators (DNOs) regulates energy intake according to peaks and lows in energy demand. Operating that way DNOs provide more reliable services to their customers.

Electrical Vehicles (EVs) simply plug and charge taking from the grid all the energy they need to. Smart charging allows to the grid operators to optimize energy flow into EVs. When needed smart charging reduces demand on the grid as a form of demand response.

Thanks to demand response, Charge Point Operators (CPOs) act with Distribution Network Operators (DNOs) in adjusting the energy demands of the EV charge point network.

===EV charging - Artificial Intelligence-based Load Management===

====Principle====
Advanced AI-based EV charging management is used to generate an optimal dynamic setpoint based on a number of criteria. These include electric vehicle planning, grid energy tariffs, prediction of building consumption, and prediction of local energy sources production, if any.

Advanced AI-based EV charging management usually relies on two techniques: [[#Forecasting energy consumption, EV charging needs and local production|forecast]] and [[#Model Predictive Control|model predictive control]].

{{FigImage|DB431451_EN|svg|EV49|Advanced AI-based EV charging management with forecasting and predictive control}}

====Forecasting energy consumption, EV charging needs and local production====

A forecasting component is used to predict local energy demands and local energy production. This enables short-term energy resource planning and optimized local energy use.

The forecasting component uses supervised machine learning techniques to learn the relationship between the variables at hand and the variable we intend to forecast.

Photovoltaic production forecasting is similar to the solar radiation forecast provided by a weather forecast service.

Building energy consumption and EV charging needs can be forecast based on historical energy consumption that identifies recurrent patterns. This forecast can be improved by adding additional drivers like weather forecast information or EV charging planning. 

The accuracy of the forecast is critical for an optimal Model Predictive Control.

====Model Predictive Control====

Model Predictive Control (MPC) techniques can be used to optimize energy usage over the following 24 hours, by anticipating energy demands (EV charging and other loads) as well as local renewable production.

The Model Predictive Controller relies on:

*A model with the description of the electrical network, the assets’ characteristics and constraints that should be respected.
*Forecasts over the following 24 hours including the energy consumption of the installation, EV charging demand, photovoltaic production, and grid energy tariffs.
*Knowledge of the assets’ current state, for example, the state of charge of the electric cars or Energy Storage System.

By updating the local controller based on latest site measures, and updating forecast information every 15 minutes, the AI-based management can continuously adapt to prediction and model errors, to ensure optimal closed-loop control performance.

====Optimize usage with local source / microgrid / demand response====

Microgrids are integrated energy systems consisting of a group of interconnected Distributed Energy Resources within clearly defined electrical boundaries that act as a single controllable entity with respect to the grid.

{{FigImage|DB431452_EN|svg|EV50|Elements of a microgrid}}

New EV loads need to be managed according to the operational activity of the building, and at the same time they have to be coordinated with the other energy sources like photovoltaic or battery storage, in order to optimize energy as well as energy-related costs.

In such a scenario, two systems are required to efficiently manage this group of Distributed Energy:

*A Load Management System is needed to control and split the EV chargers power demand and to manage the charging priority of the fleet.
*A Microgrid Advisor Solution is required to manage the energy of the different sources, based on the forecast of the fixed loads of building energy and to manage the flexible loads (such as EV and HVAC), to optimize the contract with the utility.

===Integration of EV charging into a Building Management System (BMS)===

The building management system (BMS) is a critical tool for operating a building safely, efficiently, and reliably. However, a higher expectation on energy efficiency and sustainability combined with fundamental changes in tenant needs are straining traditional BMS implementations, pushing them to grow and evolve. At the same time, advancements in cloud computing, IoT, analytics, and artificial intelligence are leading to new and broader capabilities. With these as underlying technologies, next generation BMSs become the integration and aggregation tool for all the building’s data across multiple business and operations technology systems and sensors.

Sometimes a traditional BMS integrates with other systems, but usually this just means data points are pulled from the system and displayed in the BMS software for added context or situational awareness. Next generation BMSs take this integration much further. Not only does it interact with more systems, but the connection is more tightly integrated in that the data can be combined with other system data and used for analytics, AI, and digital services that make operations more proactive and predictive.

To improve the energy usage and accountability of the building, the EV loads need to be integrated in the next generation of BMSs.

{{FigImage|DB431455_EN|svg|EV51|The scope of BMS implementations and depth of integration with other systems is evolving}}

==EVSE Asset management==

Asset management refers to the process of installing, operating, maintaining in a cost-effective manner. Most commonly used in finance, the term is used in reference to individuals or firms that manage assets on behalf of individuals or other entities.

Every infrastructure owner, individuals or companies needs to keep track of its assets.

Asset management is about processes to optimize cost of my assets (CAPEX + OPEX) to support my business.

With the advent of Digital Solution and Internet of Things (IoT), asset manager often rely on dedicated software platforms to support their processes.

{{Highlightbox|text=
Asset management is key for Charge Point Operators to maximize chargers availability and revenues associated, while reducing their costs.
}}

The asset management activity is built around 2 main families of services which are described in the table below:

{{tb-start | id=TBxx | num=EV52 | title=Asset management activity main families of services | cols=4 }}
{| class="wikitable"
| style="vertical-align: middle; background-color:#3dcd58; color: white;" |<big>Asset  Performance Management</big>
| style="vertical-align: middle; background-color:#42b4e6; color: white;" |<big>Enterprise  Asset Management</big>
|-
| {{tb-HC2}} |'''Asset Strategy and Risk Management'''
| {{tb-HC2}} |'''Maintenance Scheduling'''
|-
|Aggregation, analysis and correlation of asset information based on usage, status, health to optimize Opex, reduce risk, and help optimize Capex over the long the term
|Assembling and coordinating the information, people, materials, equipment, along with all the other necessary  resources (software planning, remote monitoring, etc.)
|-
| {{tb-HC2}} |'''Reliability-Centered Maintenance'''
| {{tb-HC2}} |'''Processes centralization'''
|-
|Provides a structured framework for analyzing the functions and potential failures for a physical asset with a focus on preserving system functions, rather than preserving equipment
|Centralizing workflows to perform maintenance and run operations in order to standardize and  increase efficiency (e.g. cloud hosted solution to standardize across organization)
|-
| {{tb-HC2}} |'''Predictive & condition based maintenance'''
| {{tb-HC2}} |'''Data aggregation for Equipment'''
|-
|Monitors the actual condition of an asset to decide what maintenance needs to be done and predict the likelihood of future failures and determine asset failure factors that could impact plant or business operations
|Component and asset tracking, health status, compliance, lifecycle management, update asset record, etc.
|-
| {{tb-HC2}} |'''Condition monitoring'''
| {{tb-HC2}} |'''Data aggregation for mobile workforce'''
|-
|Real-time measurements  (e.g. charging power, temperature, or vibration) on a piece of equipment
|Skills  management, enablement of remote assistance based on capabilities (AR, VR,  Remote services), etc.
|}

'''Asset management applied to EVSE'''

EVSE asset managers will build their management plan with a subset of the upper-listed services by balancing the risk on the operational performance of the assets against its life-cycle cost. 

* '''Individual, stand-alone stations''' tend to require relatively little maintenance over the course of their lifetimes. Typically, these EV chargers do not need many repairs or maintenance in and of themselves.
* '''Charging stations, which are in public spaces and parking lots''', require more attention. Because these are larger units with more components, the chances that an individual component malfunctions is somewhat higher than with a privately owned charger. Depending on the usage, the socket which is installed with the units could be replaced periodically
* '''DC Fast Charging stations''' will certainly require more maintenance and repair over time. In fact, these units require continual maintenance due to the complexity. Superchargers require filers, cooling systems, and other advanced parts to function properly. Operators of such charging stations must work with manufacturers to establish a service program ahead of installation, as the extent of the electric vehicle charging station maintenance you’ll require will vary based on location and anticipated frequency of use.

Several factors can play a role in the condition of the unit and the degree of EV charger repair you’ll need over the lifetime of the unit, including frequency of use, climate, and whether the unit is covered or exposed to the elements. Generally, the units should be kept clean by wiping them down with a damp cloth, and any accessible parts need to be checked on occasion for basic wear and tear.

Therefore, '''having a plan to protect your EV charging assets is key to the success of your charging infrastructure'''.

This plan includes various activities which can be done on site or remotely:

* Error diagnostics and troubleshooting
* Root cause failure analysis — Actions taken to determine why a particular failure or issue exists and correcting those causes
* Upgrading the charging station with the latest firmware and benefitting from additional features
* Restoration of factory default settings
* Changing spare parts

=== Corrective maintenance ===

Corrective maintenance is the category of maintenance tasks that are performed to rectify and repair faulty systems and equipment. The purpose of corrective maintenance is to restore systems that have broken down. 

'''Corrective maintenance in the case of EVSE'''

In many cases, a local electrician can troubleshoot problems with the units. Software Platforms also offer remote options that can reduce long-term maintenance costs.

When product is connected, an alarm is created in the logs and the team in charge of the maintenance could be notified to manage this event.

When an issue is raised, the first task of the maintenance is to diagnose the problem usually with some specific tools.

Then, there are mainly 3 ways to solve an issue as per the corrective maintenance of charging station:

* '''Hardware change''': According to the type of products and type of defect, we see either a replacement of the charging station, especially with basic Wallbox or a replacement of a specific part (socket, RFID reader …)
* '''Configuration update''': A defect could be triggered by a misconfiguration of the product. Changing the configuration of the product is sometimes a way to fix a defect
* '''Software update:''' Issue are sometimes due to bugs in the firmware of the charging station. Updating to a new firmware solve bugs and usually fix problems

Configuration and firmware update could be done on site or remotely if the product is connected. When products are connected, maintenance could be done product by product or simultaneously on set of products which face the same issue.

=== Preventive maintenance ===

Next level of maintenance is to prevent any unplanned downtime and expensive costs because of unexpected equipment failure. Preventive maintenance requires careful planning and scheduling of maintenance on equipment before a problem arises, as well as keeping accurate records of past inspections and servicing reports.

As you can see on the graph, the optimal zone is when you have an effective preventive maintenance tailored to the products that you are managing. This mode of maintenance would be even more efficient when the charging station is connected to a remote maintenance platform.

{{FigImage|DB431456_EN|svg|EV53|title=Optimization of the assets total maintenance cost}}

'''Preventive maintenance in the case of EVSE'''

Having charging station connected to a remote platform provide reports which track usage, performance, and efficiency to help you better understand which units are being used with the most frequency as well as which units are performing optimally and which are not. You can also tap into the power of the users: with connected solutions, drivers could report violations, station misuse, or maintenance concerns so you can address EV charger repair issues before they become more serious problems}}.

Having a digital logbook is the right approach for an efficient preventive maintenance. The digital logbook is a collaborative tool that keeps record of important documentation and maintenance schedules.

The creation of the Digital Logbook ensures the availability of project lifecycle documentation, including the single-line diagram, maintenance plan, and more.

* Track your assets for long-term maintenance schedules and task reminders
* Log and access asset history, maintenance procedures, and collaborative information
* Generate inspection and activity reports
* Identify maintenance status

=== Predictive maintenance ===

Predictive maintenance technologies enable companies to perform an effective amount of maintenance at an appropriate or practical time. Often referred to as condition-based maintenance, predictive maintenance tools monitor the condition of in-service equipment, either continuously (connected products) or at periodic intervals. Having regular access to the current state of the equipment provides valuable information, making it possible to reduce the disruptions of the EV charging infrastructure.

'''Predictive maintenance in the case of EVSE'''

Estimating and projecting equipment condition over time will help to identify particular units that are most likely to have defects requiring repairs. Such an exercise will also identify units whose unique stresses (i.e., a charging station that have a high number of sessions, that often face issue to lock a cable) have an increased probability of future failure. A condition-based maintenance method also identifies, through statistics and data, which equipment components most likely will remain in acceptable condition without the need for maintenance.

Maintenance can therefore be targeted where it will be more effective.

Condition-based maintenance data that is useful and available to help estimate the condition of the equipment includes the following:

* Age 
* History of operating experience
* Environmental history (temperature, voltage, run-time, abnormal events) 
* Operating characteristics (private vs public, low activity vs High activity…)