FR3092795A1 - Electrical energy management system - Google Patents

Abstract

The invention relates to a vehicle comprising: - a battery (B) - at least one electric motor suitable for propelling the vehicle, - at least one thermal installation (T) comprising at least one element of the vehicle to be heated or cooled, and / or at least one source of calories or frigories, said source comprising at least one electrical consumer, and / or at least one storage of calories and / or frigories, and / or suitable control and / or command means to distribute the calories or the frigories available at the level of the sources to the elements to be heated or cooled, according to the transient or nominal needs of the vehicle, - means of prediction (PR), - means (GE) of battery management Figure to be published with the abstract: Figure 1.

Description

Electrical energy management system

Technical field of the invention

The invention relates to a vehicle, in particular a motor vehicle, as well as an electrical energy management system.

State of the prior art

Due to environmental concerns and increasingly stringent regulations, the automotive industry is actively participating in the development and commercialization of electric vehicles. This continued development suggests a significant increase in electricity demand in the future. Under the same environmental constraints, the growth of electricity production is forced to rely on new sources of renewable and clean energy. In order to support both new unconventional energy sources and electric vehicles, the power grid structure is undergoing rapid structural improvements and upgrades. In particular, many countries are on the verge of having a first generation of smart grid.

A typical electrical network is characterized by a simple and unidirectional energy structure. The power plants play the role of the electrical energy source and the transmission infrastructure conveys the generated electrical energy to the consumption sites. This simple model generally relies on stationary central sources, such as nuclear and thermal power plants. It is relatively technically difficult to adjust the energy production of these sources in order to respond dynamically to the variable demand of the consumption of the network, in particular in the event of a period of peak consumption. Moreover, this classic structure is not well suited to energy sources with irregular production capacities over time, such as renewable energy sources (wind turbines, photovoltaic panels).

The use of an intelligent network (smart grid in English) makes it possible to bring flexibility to a traditional structure of the electrical network. For this, in addition to the conventional structure comprising sources, means of transport and consumption sites, an intelligent network allows the integration of renewable energy sources and energy storage elements. All these components are able to communicate with each other via data transmission lines or channels. Remote management means, or control center, centrally execute a management mode based on data such as the collection and dissemination of the price, data relating to power and/or energy demand, control, etc.

Despite the complexity of such a smart grid, it has a key environmental and economic advantage over a conventional power grid.

The irregular production of energy due to the use of renewable energy sources, and the irregular consumption of electricity imply the need to provide a large capacity energy storage structure.

There are different energy storage techniques (such as hydrogen storage, thermal storage, freewheeling, etc.). Most of these solutions have not yet reached a sufficient degree of technological maturity to be applied on a large scale in a smart grid context.

Until a few years ago, battery technology was also considered an inefficient and expensive solution, due to its rapid aging. Such aging is caused by various factors, such as poor thermal conditions, increased operating cycle and battery degradation over time.

Nowadays, thanks to the progress made in the development of batteries (especially Lithium batteries), it is possible to reconsider the battery as a viable and affordable storage solution. Two storage solutions were considered.

A first solution is the installation of fixed and independent battery storage units.

In this case, batteries are installed for the sole purpose of providing energy storage to serve the smart grid. They can be used in a global or local approach, for example for local residential, commercial or public areas.

A second solution, described in particular in document US Pat. No. 9,739,624, is the use of electric vehicle batteries to supply the smart grid with electrical energy.

Indeed, with the evolution of the automotive market towards the electric vehicle, it is tempting to consider using the electric vehicle both as a transport tool and as a storage unit for the smart grid. The recent existing approach is based on planning the use of the vehicle in order to ensure the two services. Once parked and plugged in or connected to the electrical grid, the vehicle's batteries can be charged or discharged depending on, among other things, the rate of charge of the battery, the demand of the smart grid and/or the cost of energy at a given time. . The vehicle battery, for example, stores electricity at low cost, when the demand for energy from the grid is low or there is a surplus of energy production in the grid, and the energy thus stored can be resold when the price of energy is higher, whether there is a high demand in the network or the energy production of the network is lower. The disadvantage of such an approach is that it is only valid for electric vehicles with well-defined driving schedules, such as company vehicle fleets or private vehicles for residential use.

Another parameter limiting the overuse of batteries is their aging. Indeed, such use implies a high rate of use, that is to say a high charge and discharge amplitude, and many successive charge and discharge cycles, which promotes premature aging of the battery. Lithium.

Presentation of the invention

The invention aims to remedy all or part of the aforementioned drawbacks, in a simple, reliable and economical manner.

To this end, the invention relates to a vehicle comprising:
- a battery capable of storing and delivering electrical energy,
- at least one electric motor suitable for propelling the vehicle,
- at least one thermal installation comprising at least one of the following elements:
y. at least one part of the vehicle to be heated or cooled,
y. at least one source of calories or cold, said source comprising at least one electrical consumer,
y. at least one calorie and/or cold store, and/or means for isolating the battery or the cabin of the vehicle, and/or means for dynamically isolating the battery or the cabin of the vehicle,
y. control and/or command means capable of distributing the calories or cold temperatures available at the sources to the elements to be heated or cooled, according to the transient or nominal needs of the vehicle,
- prediction means capable of carrying out at least one prediction aimed at determining the future need of the energy consumer of the thermal installation or of the electric motor of the vehicle over a given period of use or between two consecutive recharges of the battery,
- battery management means capable of determining a part of the capacity of the battery which is reserved for said energy consumer and for said electric motor of the vehicle and another part of the capacity of the battery which can be used to supply a intelligent network, depending in particular on the prediction made by the prediction means and/or depending on data external to the vehicle.

In this way, part of the battery capacity can be reserved for the vehicle, the other part of the capacity can be used to be connected to an intelligent network in order to supply it when needed, in particular during consumption peaks for example. The part of the capacity that can be allocated to each of these functions can vary, and can thus be adjusted by the management means, so as to limit in particular the amplitude and/or the frequency of the charge and discharge cycles and thus control the aging of the battery.

The battery or cabin insulation means may comprise at least one layer of thermally insulating material.

The means for dynamic insulation of the battery or of the passenger compartment may in particular comprise at least one layer of thermally insulating material, associated with at least one element capable of storing and/or delivering calories and/or cold temperatures, made of material phase change (PCM).

Means for dynamic insulation of the passenger compartment are known in particular from document WO 2017/153693, in the name of the Applicant.

Means for dynamic isolation of the battery are known in particular from document WO 2017/153691, in the name of the Applicant.

Data external to the vehicle may be provided by an electrical energy management installation.

The element to be heated or cooled may comprise the battery and/or the electric motor used to propel the vehicle.

Thermal conditioning of the battery also greatly improves battery life.

The battery and/or the electric motor can also form a source of calories.

More generally, the structure of the thermal installation makes it possible to limit the quantity of electrical energy necessary to ensure the thermal conditioning of the passenger compartment and of the various elements of the vehicle, and therefore limits the use of the battery for this alone. function. The capacity of the battery can then be used usefully to ensure other functions, in particular the supply of a smart grid with electrical energy.

The prediction means make it possible to adapt the operation of the thermal installation, not only to the data and constraints of the various elements of the installation detected in real time, but to predictions making it possible to adapt the behavior of the installation to constraints or subsequent opportunities to come, such as the availability or the subsequent lack of calories or cold at a source for example.

In addition, the control and/or command means make it possible to adapt the operation of the thermal installation according to:
- transient needs, these are needs occurring over a short period of time to lead to nominal functioning in a state of equilibrium;
- nominal needs consisting in maintaining a state of equilibrium.

The predictions can in particular be made by taking into account the data collected relating to the profile of each user.

In general, the thermal installation reduces the aging of the battery and improves its performance/

The vehicle may be a motor vehicle.

As a variant, the vehicle can be an aircraft, in particular an airplane, or a train.

The prediction means may be able to calculate the subsequent availability of calories and/or cold temperatures, and/or the subsequent calorie and/or cold needs from at least one of the following input data:
- data related to the user's habits and/or comfort preferences, in particular:
y. data related to the temperature of the vehicle cabin usually desired by the user,
y. air flow usually desired by the user in the passenger compartment,
y. distribution of the air flow usually desired by the user in the passenger compartment, between different points of entry of the air into the passenger compartment, such as vents,
y. distribution between the fresh air outside the passenger compartment and the recycled air from the passenger compartment, usually desired by the user,
y. orientation of the aerators usually desired by the user,
- data related to the user's driving habits and/or preferences, in particular:
y. vehicle speed data,
y. vehicle driving time,
y. vehicle downtime,
y. vehicle acceleration,
y. speed of a heat or electric motor of the vehicle,
- data related to the user's journey, in particular:
y. geographical coordinates of the place of departure and/or the place of arrival planned or provided by the user, with the elevations traveled possibly allowing the recovery of energy.
y. real-time geographical coordinates of the vehicle,
y. meteorological data, such as the speed and direction of the wind, the temperature, the rainfall, the hygrometry, in particular on the route, the place of parking and/or the place of arrival planned,
y. traffic conditions on the route,
- data related to the state of charge of a battery, in particular:
y. battery charging type, such as fast charging or normal charging,
y. expected charging time.

The thermal installation may include management means capable of:
- define a thermal requirement for one or more of the elements of the vehicle to be heated or cooled, from input data linked to the state of the elements of the vehicle to be heated or cooled, to the state of the heat sources or cold temperatures, a user request and/or a prediction,
- define an operating mode of the thermal installation from said thermal need,
- Actuate actuators of the thermal installation, so as to operate the thermal installation according to the selected operating mode.

The actuators can comprise for example at least one pump, at least one compressor, at least one controlled valve and/or at least one electric resistor.

The thermal need is for example the need to heat or cool one of the elements of the thermal installation or of the electric vehicle, such as for example a battery, an internal combustion heat engine, or electrical or electronic components. The thermal requirement can also include information on the intensity of the heating or cooling to be carried out. The thermal requirement can also be neutral, i.e. requiring no heating or cooling of the element concerned. A thermal requirement can be associated with each of the elements to be heated or cooled in the thermal installation.

The thermal installation may comprise regulation means capable of regulating actuators belonging to the installation and making it possible to distribute the calories or the cold temperatures available at the sources to the elements to be heated or cooled. The regulation means act dynamically, so as to maintain a measured or calculated value, for example the temperature of an element of the thermal installation or of the vehicle, close to a set value.

The regulation means may comprise a PID regulator, also called a PID (proportional, integral, derivative) corrector, a predictive control (MPC for Model Predictive Control), a fuzzy logic or optimal control controller. Such regulation means are known from the field of automatic control and their operation will not be explained in detail.

The prediction means of the thermal installation can use theoretical models and/or learning models also called "Machine Learning", for example using an artificial neural network, or can use databases data and be based on pre-existing data. It is also possible to use a model based on mathematical equations simulating the behavior of the various elements of the installation or vehicle.

At least one source of calories and/or cold temperatures also forms an element to be heated or cooled, depending on the operating conditions of the vehicle.

The installation may include at least one calorie storer and at least one cold store.

The storage means may comprise a phase change material, for example water, glycol, saline solution or paraffin. In particular, the thermal phase change material (PCM) may consist of n-hexadecane, eicosane or a lithium salt, all having melting points below 40°C. Alternatively, the MCP material may be based on a fatty acid or a eutectic or hydrated salt, or even fatty alcohols, for example. Such thermal storage means make it possible to accumulate thermal energy (calories or frigories) by latent heat (phase change) or by sensible heat.

The battery can be mounted in a casing housing a phase-change material capable of storing calories and/or cold temperatures.

The installation may include a device for heating, ventilation and/or conditioning of a passenger compartment of the vehicle having a structure similar to that described in patent application FR 3057494, in the name of the Applicant.

In particular, the thermal installation may comprise a device for heating, ventilation and/or conditioning of a vehicle passenger compartment, comprising:
- a refrigerant circuit,
- a heat transfer fluid circuit,
- a first heat exchanger capable of exchanging heat between the heat transfer fluid and the air intended to emerge into the passenger compartment of the vehicle,
- a second heat exchanger capable of exchanging heat between the refrigerant and the air intended to emerge into the passenger compartment of the vehicle and capable of forming a condenser,
- a third heat exchanger capable of exchanging heat between the refrigerant and the air intended to emerge into the passenger compartment of the vehicle and capable of forming an evaporator,
- at least a fourth heat exchanger capable of exchanging heat between the refrigerant and the heat transfer fluid,
- the control means being capable of distributing the calories or the cold temperatures between the sources and the elements to be heated or cooled, through the refrigerant circuit, the heat transfer fluid circuit and/or said exchangers.

The heating, ventilation and/or conditioning device may comprise a fifth heat exchanger capable of exchanging heat between the heat transfer fluid or the refrigerant fluid, on the one hand, and hot gases from an exhaust line of the vehicle , on the other hand.

The battery may be capable of exchanging heat with the heat transfer fluid.

The storer may be capable of exchanging heat with the heat transfer fluid.

The thermal installation may include a heat engine capable of exchanging heat with a heat transfer fluid, for example oil. The heat engine can be at least one element of the vehicle to be heated or cooled and/or at least one heat source.

The thermal installation may comprise at least one electrical resistor capable of exchanging heat with the air intended for the passenger compartment of the vehicle or with the aforementioned heat transfer fluid.

The thermal installation may comprise at least one electrical component capable of exchanging heat with a heat transfer fluid, the electrical component being an electrical machine and/or a power module capable of forming a heat source.

The thermal installation may comprise means capable of ensuring the pre-conditioning of the passenger compartment before the user enters the vehicle.

The invention also relates to an electrical energy management system comprising:
- an intelligent electrical energy network,
- a database comprising information relating to the electrical consumption of a plurality of vehicles,
- management means capable of determining or receiving information relating to the needs of the intelligent network, information relating to the electrical consumption of the vehicles, and authorizing the distribution of electrical energy between sources of electrical energy of the vehicles connected to the intelligent network and electrical energy receivers of the smart grid and/or between electrical energy sources of the smart grid and electrical energy receivers of vehicles connected to the smart grid,

the management means providing information to vehicles of the aforementioned type, so as to dynamically adjust the part of the battery which is reserved for the energy consumer and the electric motor of the vehicle, and the part of the battery which can be used to feed the smart grid, using data from the database.

The intelligent electric power network is also called smart grid, in English.

The dynamic adjustment of the threshold between the two portions of the battery can be carried out in real time or by more or less long periods of time, for example every month, every week, etc.

The batteries of the vehicle fleet form a reserve of energy capacity that can be connected to the smart grid, to supply said grid when needed. The batteries can also be recharged by smart grid electrical sources.

The number of vehicles in the fleet can be very large, for example greater than several thousand or million vehicles.

The system management means may be able to determine:
- whether or not the smart grid is able to receive all or part of the electrical energy available at the level of the batteries of the vehicles connected to the smart grid.
- whether or not the smart grid is able to supply the desired electrical energy to power the vehicles connected to the smart grid.
- whether or not the smart grid needs the storage capacity available in the batteries of the vehicles connected to the smart grid. These management means can authorize and make available to the intelligent network all or part of this accumulated capacity for the purpose of providing ancillary electrical services.

The system management means may comprise selection means capable of selecting the vehicles intended to exchange electrical energy with the intelligent network, as a function in particular of the rate of charge of the battery of each vehicle, of the distribution between the said portions and/or the state of health of each vehicle's battery.

The management means can authorize the exchange of energy between the vehicles of a local fleet or remote vehicles, if the infrastructure allows it.

The management means may include prediction means aimed at determining a predicted model of the electrical consumption of the vehicle over a given period of use or between two consecutive recharges of the battery.

The predicted model may include or use a distribution law or a probability law of said electrical consumption of the vehicle.

The model can be built in real time and therefore be adjusted according to the actual use of the vehicle.

The prediction means may be able to
- determine the cumulative capacity Cu of the vehicle, that is to say the accumulated energy used between two charging sessions, in several cases of use of the vehicle,
- optionally, determining at least one density estimate E(Cu) of the cumulative capacities,
- determining the predicted model on the basis of the cumulative capacity Cu and/or the density E(Cu).

The cumulative capacity can be defined as the accumulated energy used between two charging sessions (every T hours):

or :

, SoC being the battery charge rate.

In other words, the cumulative capacity Cu represents the cumulative discharged capacity in the time interval [0, T]. It represents the battery usage regardless of the occurrence of recharge in the interval [0, T]. This variable helps to indicate the energy requirement in the interval [0, T] regardless of the unplanned recharge.

The management means may include control means making it possible to control whether the use of the vehicle corresponds to the predicted model.

If the actual use of the vehicle does not correspond to the predicted model, then the predicted model can in particular be readjusted in real time.

The management means can communicate with the vehicles via a telecommunications network.

The telecommunications network may in particular allow data exchanges in accordance with one or more of the following protocols: GSM, GPRS, EDGE, 3G, LTE, LTE-Advanced, 4G.

The database may include information relating to the electrical consumption of at least one vehicle managed by the installation, depending on at least one of the following parameters:
- the state of thermal load of a calorie and/or cold store of the vehicle,
- the state of charge of the vehicle battery,
- the state of health of the vehicle battery,
- the vehicle user profile,
- the identity of the vehicle user,
- the type of vehicle,
- meteorological data linked to the vehicle,
- the date of use of the vehicle,
- the location of the vehicle,
- the traffic situation in the geographical area of the vehicle,
- the time habits of the vehicle user,
- the habits related to the comfort of the user of the vehicle,
- the state of charge of a calorie and/or cold store of the vehicle.

Brief description of figures

schematically illustrates an electrical energy management system according to one embodiment of the invention,

schematically illustrates part of said system,

schematically illustrates the distribution of the different allocated parts of the battery,

is a diagram illustrating the evolution of the battery charge rate as a function of time,

is a diagram illustrating the evolution of the battery charge rate as a function of time,

is a diagram illustrating the distribution of cumulative capacities as a function of time, all days of the week taken together;

is a diagram illustrating the distribution of cumulative capacities as a function of time, excluding weekends and public holidays,

is a diagram illustrating the distribution of cumulative capacities as a function of time, for weekend days and public holidays,

is a diagram illustrating the evolution of the density according to the cumulated capacity,

is a diagram representing the evolution of the probability according to the cumulated capacity,

is illustrates a management system according to one embodiment of the invention,

illustrates a battery used in the invention,

is a diagram illustrating the operation of the management system,

is a diagram illustrating the operation of the management system,

is a diagram illustrating the operation of the management system.

Detailed description of the invention

Figure 1 schematically illustrates an electrical energy management system according to one embodiment of the invention.

This includes:
- an intelligent REI electrical energy network,
- a database BD comprising information relating to the electrical consumption of a plurality of electric vehicles VE (totally electric or hybrid),
- management means GD capable of determining or receiving information relating to the needs of the intelligent network, information relating to the electrical consumption of vehicles, and authorizing the distribution of electrical energy between sources of electrical energy of the vehicles connected to the intelligent network and smart grid electric power receivers and/or between smart grid electric power sources and smart grid connected vehicle electric power receivers.

These aforementioned management means GD are not on board the vehicles VE and are said to be remote.

Each EV vehicle includes:
- a battery B capable of storing and delivering electrical energy,
- at least one electric motor suitable for propelling the vehicle,
- at least one thermal installation T comprising
y. at least one part of the vehicle to be heated or cooled,
y. at least one source of calories or cold, said source comprising at least one electrical consumer,
y. at least one calorie and/or cold store, and/or means for isolating the battery or the cabin of the vehicle, and/or means for dynamically isolating the battery or the cabin of the vehicle,
y. control and/or command means capable of distributing the calories or cold temperatures available at the sources to the elements to be heated or cooled, according to the transient or nominal needs of the vehicle,
- prediction means PR capable of carrying out at least one prediction aimed at determining the future need of the energy consumers of the thermal installation T of the vehicle over a given period of use or between two consecutive recharges of the battery B, these prediction means PR being called on-board prediction means,
- battery management means GE able to determine a part P1 of the capacity of the battery which is reserved for the energy consumer and the electric motor of the vehicle and another part P2 of the capacity of the battery which can be used to supply an intelligent network, according in particular to the prediction made by the prediction means and/or according to data external to the vehicle. These management means GE are on board the vehicle, unlike the remote management means GD.

The remote management means provide information to the vehicles, so as to dynamically adjust the part of the battery which is reserved for the energy consumers of the vehicle and the part of the battery which can be used to supply the intelligent network, from data from the database.

The database BD may in particular include information relating to the electrical consumption of at least one vehicle managed by the installation, depending on at least one of the following parameters:
- the state of charge of the vehicle battery,
- the state of health of the vehicle battery,
- the vehicle user profile,
- the identity of the vehicle user,
- the type of vehicle,
- meteorological data linked to the vehicle,
- the date of use of the vehicle,
- the location of the vehicle,
- the traffic situation in the geographical area of the vehicle,
- the time habits of the vehicle user,
- the habits related to the comfort of the user of the vehicle,
- the state of charge of a calorie and/or cold store of the vehicle.

In general, as illustrated in FIG. 2, the capacity of the battery is maintained between a minimum capacity Cmin, corresponding for example to 20% of the total capacity CT of the battery, and a maximum capacity Cmax, corresponding for example to 80 % of the total CT capacity of the battery, so as to avoid any premature degradation of the battery. The difference between the maximum capacity and the minimum capacity is the TUB battery usage rate.

In urban use of the vehicle, the battery is largely oversized for daily use. The remote and on-board management means make it possible to define which part P1 of the total capacity CT of the battery is truly useful for the daily use of the vehicle, and which part P2 can be used for other purposes, in particular to supply the smart grid, as shown in Figure 3.

The structure of the thermal installation of the vehicle according to the invention, as well as its on-board prediction and management means, make it possible to increase the overall performance by reducing the energy required to ensure the operation of the vehicle and the comfort of its user. . The operation of such a thermal installation is described in particular in patent application FR 3057494, in the name of the Applicant.

The diagram in FIG. 4 represents the evolution of the charge rate (SoC) of the battery as a function of the duration of use t of the vehicle, for a configuration of use, both for a conventional vehicle, and for a vehicle according to the invention, the thermal installation of which includes means for cooling the battery.

There is a significant improvement in the SoC charge rate of the battery, for example an improvement of about 15% after 4 hours of use.

For the same battery capacity, such a performance improvement increases the part P2 of the battery that can be allocated to the smart grid.

The remote and/or on-board management means comprise selection means able to select the vehicles intended to exchange electrical energy with the intelligent network, depending in particular on the rate of charge of the battery of each vehicle, the distribution between said portions P1, P2 and/or of the state of health of the battery of each vehicle.

The remote management means communicate with the vehicles via a telecommunications network.

The remote management means comprise prediction means, called remote prediction means.

The remote and/or on-board prediction means aim to determine a predicted model of the electrical consumption of the vehicle over a given period of use or between two consecutive recharges of the battery.

These prediction means use prediction models or statistical models making it possible to extract the driving data patterns for a driver and/or vehicle. From these, usage schedules can be defined. When applied to vehicle driving data, these tools or features fine-tune the portion P1 of the battery reserved for vehicle use, leaving a sufficient portion P2 of the battery capacity for use. of the vehicle as an energy storage reserve connected to the smart grid.

The predicted model behaves according to a statistical distribution of said vehicle electrical consumption.

The model can be built in real time and therefore be adjusted according to the actual use of the vehicle.

To make such a prediction, it is possible to use a variable that we will call cumulative capacity.

Suppose the vehicle is connected to the smart grid at least once (or on average) every T hours (eg, T = 6 p.m.). We define the cumulative capacity utilization Cu as the accumulated energy used between two charging sessions (every T hours):

or :

In other words, the cumulative capacity Cu represents the cumulative discharged capacity in the time interval [0, T]. It represents the battery usage regardless of the occurrence of recharge in the interval [0, T]. It helps to indicate the energy requirement in the interval [0, T] regardless of unplanned recharge.

FIG. 5 is a diagram representing the evolution of the SoC charge rate of the battery as a function of time. In the example illustrated in this figure, in the time interval between 0 and a, Cu _0a = 15%. Moreover, in the time interval between b and T, Cu _bT = 5. Cu _ab is itself equal to 0 because the battery was charged in this time interval and not discharged. In the time interval between 0 and T, Cu _0T is therefore equal to 20%.

In modern connected electric vehicles, this variable is easily calculable.

It is also possible to define a cumulative discharge capacity specific to the electrical consumption linked to the comfort function (thermal conditioning of the cabin, in particular) supplied to the user of the vehicle, as well as a cumulative discharge capacity specific to the electrical consumption related to the propulsion of the vehicle. This makes it possible to define, for the same user, a profile linked to driving and a profile linked to comfort.

One method to define the required cumulative capacity of frequent use for a given driver profile is to use dataset tools such as density estimation. The density distribution can be used to determine the probability describing the sufficiency of battery energy or capacity for the profile or for the determined data.

For example, consider FIG. 6 where the abscissa represents the measured cumulative capacity Cu _i for each round trip (for example, between two charging stations or periodically for T hours). Each black line represents a point measured Cu _i for a trip. The total number of points corresponds to the number of these trips over a long period, such as 6 months or a year.

Note that the profile shown in Figure 6 shows frequent battery usage, indicated by the cluster dots, between 20% and 30% of total capacity CT on the one hand, and around 60% of CT on the other. of cumulative capacity. Using data analysis tools, these points were examined to find groups of points or clusters that could be separated or classified. These distinct groups can be linked to different driving behaviors between typical weekdays (Figure 7) and weekends or holidays (Figure 8). Therefore, the data can be reorganized into different case studies with unique groups.

For each case and on the basis of the data obtained, a corresponding density estimate, denoted E(Cu) is derived as a function of Cu. Figure 9 illustrates the estimation of the density E of the weekday-based profile data from Figure 7 as a function of the cumulative capacity Cu.

Let Cu _{s be} an unknown that represents exactly the sufficient capacity of Cu required for a typical trip. The probability that the required capacity Cu _s is less than a _{given cumulative capacity Cu i} (for example Cu _i = 40%) is expressed as follows:

In other words, the probability represents the area bounded by the density curve E, the horizontal axis Cu and the vertical line Cu = 40. For each point Cu _i , the probability P is then obtained as shown in figure 10. Conversely, by adapting a constraint given such as an 80% probability of use cases, we can obtain the _{corresponding Cu i} (in the case of an 80% probability, Cu = 40%). In other words, in 80% of use cases, 40% of the battery's maximum CT capacity would suffice.

For better representability, the data can be studied independently in various contexts and situations, such as Mondays, weekends, holidays, climatic seasons, different states of the climate, etc. This makes it possible to find more suitable and dynamic driving capacity utilization thresholds. In addition, the data may have additional dimensions such as, for example, temperature.

The remote and/or on-board management means comprise control means making it possible to control whether the use of the vehicle corresponds to the predicted model. The model can be adapted if the actual usage does not match the predicted model.

In addition to the solutions described above to reduce the use of the battery, the invention also aims to manage the link between the battery and the smart grid. It is recalled that the existing approaches envisage using management based on the schedule or on the user, without it being necessary to define the capacity shares of the batteries. In these scenarios, it is up to the user to manage and decide the usage profile of the vehicle's battery, whether for driving or for powering the smart grid. Many users, such as companies with vehicle fleets, apply fleet management strategies based on pre-programmed plans. This approach is very simple and effective for a small number of vehicles. However, when it comes to a large-scale deployment for a private electric vehicle, the advance planning or human management can be very tricky and inefficient, due to the lack of predictability. On the contrary, the invention allows mass management, that is to say one that can be applied to a very large number of vehicles. For this, the energy management system according to the invention uses a centralized and automated approach. A large number of independent electric vehicles can then be managed together to create a large non-localized or decentralized storage capacity.

As illustrated by figure 11, the invention proposes to construct an energy storage in a way similar to the concept of data cloud storage, in which a large storage capacity is formed from small units of unlocated storage. The advantage, in the case of energy, is to react between the needs of users at the single user level and those of the large-scale smart grid. This centralized approach takes control of large fleets of vehicles and manages both the needs of each user and those of the smart grid. Another operational difference from the prior art is that the subject managed here is not only the energy supplied, but also the capacity involved. The battery capacity and energy of each electric vehicle is managed from a side against the needs of the global storage cloud, and cloud resources are managed according to the needs of the smart grid. The risk of energy shortage in the part of the battery dedicated to the smart grid, for each electric vehicle, decreases considerably with the increase in the number of vehicles involved in storage.

An important point for defining the user's need, designated by the storage capacity P1 of the battery dedicated to driving, consists in referring to the data collected. As previously described, this goal can be achieved through an analysis based on a BD database such as a statistical model.

It should be emphasized that, for a given vehicle, the storage capacity P1 dedicated to driving and the capacity P2 dedicated to the smart grid are not physically separated. Therefore, from a functional point of view, the vehicle can always be used outside of its statistical and intended model.

An optimal management of storage resources must drain in priority the batteries presenting the greatest margin of capacity dedicated to the smart grid and/or the highest level of energy available. This helps reduce overuse of batteries, especially those with modest capacities. This also allows very energy-intensive users to have more flexibility in the use of the electric vehicle outside the defined statistical model. Another feature is to dedicate, from the farm partition, a part P2a of capacity for long-term dedicated smart grid storage and another part P2b of capacity for short-term dedicated smart grid storage, such as shown in Figure 12. This takes the strain off the battery and increases its lifespan.

The management and control algorithm integrated into the on-board management means manages the lower level of the overall management strategy. It relies on the application of advanced predictive algorithms based on data acquired from the vehicle, user and/or remote data cloud. The on-board management means execute the local part of the storage control algorithm on the basis of data obtained directly from the vehicle, the user, the remote management means and the intelligent network when the vehicle is connected to said network.

When it is disconnected, the remote management means can act as a proxy between the vehicle and the intelligent network. The remote management means define, according to the local and remote analysis of the vehicle data, the capacity recommended for each use. The remote management means also ensure, in connection with the on-board management means, the adaptation of the capacity P2 dedicated to the intelligent network and the capacity P1 dedicated to the operation of the vehicle. Predictive control guides the management of the thermal installation and the management of the battery (BMS, Battery Management System) to anticipate energy and thermal needs. These management means improve the energy performance of the vehicle by reducing and possibly recovering part of the energy dissipated, for example in the form of heat.

The remote management means manage and balance the capacity P1 dedicated to the vehicle for all the vehicles simultaneously, independently or in batches. As shown in Figure 13, this is typically done on an evaluation request, which may be triggered by events such as user request, or periodically. Once triggered, the remote management means launch the algorithm which specifies the capacity of the battery required to ensure reliable operation of the vehicle. The algorithm can use the vehicle data and/or the vehicle simulation model to calculate the value of the required capacity threshold a _i . This value is then transmitted to the management means on board the vehicle. On the basis of this value, the on-board management means modify the capacity P2 dedicated to the intelligent network of the battery of the electric vehicle.

As shown in FIG. 14, the remote management means also manage the state of the vehicles available and connected to the intelligent network. When an electric vehicle is connected, the remote management means send control requests to unload or load the storage resources. This decision can be made based on smart grid, user and/or vehicle data. The algorithm makes the decision to discharge the batteries of electric vehicles when the demand from the network or the price of electricity is high, or according to capacity auctions for example. When this condition is met, the vehicles concerned must be selected and one of the three energy requests (recharge, discharge or neutral) is sent to each of the selected vehicles. The selection can also be optimized based on various factors, such as prioritizing the selection of vehicles with high dedicated smart grid capacities and/or high available energy. The energy demand is accompanied by a suitable or adapted specific power demand, or a predefined energy demand schedule.

On board the electric vehicle, the capacity of the battery is partitioned according to the value a _i transmitted by the remote management means. In practice, the operation of the vehicle is considered to have priority over the needs of the smart grid.

Figure 15 illustrates the algorithm of the on-board management means, i.e. integrated into the vehicle.

First, they check whether the vehicle is connected to the smart grid. If this is the case, they then check whether the load rate, denoted soc, with respect to the thresholds a _i .

When the vehicle is connected to the smart grid and if its state of charge (SoC) is lower than the threshold attributed to the propulsion of the vehicle a _i soc ≤ a _i , then the battery of the electric vehicle is not able to meet the needs of the smart grid. The capacity reserve of the battery is then fully allocated to the operation of the vehicle. In other words, the remote management means give control to battery management means on board the vehicle, which normally recharge the battery.

When the vehicle is connected to the intelligent network and if soc ≥ a _i , then the electric vehicle conforms to the request or to the energy or electrical power planning of the remote management means.

When the vehicle is not connected to the intelligent network, the management means integrated into the vehicle check whether the energy consumed 1 - soc exceeds the driving thresholds, that is to say if 1-soc > a, in other words soc < 1-a. In this case, the vehicle is considered to derive its energy from network resources. The consumption information is transmitted in real time to the remote management means (or estimated and then transmitted during the next connection). Based on the consumption information, the remote management means can anticipate the unavailability of the capacity of the vehicle.

Each time the vehicle makes a trip, the on-board management means update the data of the driving profile of the vehicle and/or of the user. This update helps readjust and re-adjust the a _i and 1-a _i thresholds when a reassessment is requested.

It is useful to specify that dynamic or multiple thresholds can be used according to different contexts of use. It is thus possible to modify a _i according to the ambient temperature or to take into account different values of a _i according to the season, the day of the week, etc. According to another characteristic, the relation between the load capacity threshold a _i in mode connected to the network and the discharge pipe capacity thresholds 1-a _i in mode not connected to the network, can be modified or cancelled. For example, it is possible to consider that the recharge load capacity threshold is a _i and the discharge pipe capacity threshold is (0.9-a _i ), instead of 1-a _i .

Claims (10)

Vehicle (VE) comprising:
- a battery (B) capable of storing and delivering electrical energy,
- at least one electric motor suitable for propelling the vehicle,
- at least one thermal installation (T) comprising at least one of the following elements:
y. at least one part of the vehicle to be heated or cooled,
y. at least one source of calories or cold, said source comprising at least one electrical consumer,
y. at least one calorie and/or cold store and/or means for isolating the battery or the cabin of the vehicle, and/or means for dynamically isolating the battery or the cabin of the vehicle,
y. control and/or command means capable of distributing the calories or cold temperatures available at the sources to the elements to be heated or cooled, according to the transient or nominal needs of the vehicle,
- prediction means (PR) capable of carrying out at least one prediction aimed at determining the future need of the energy consumer of the thermal installation or of the electric motor of the vehicle over a given period of use or between two consecutive recharges of drums,
- means (GE) for managing the battery capable of determining a part (P1) of the capacity of the battery which is reserved for said energy consumer and for said electric motor of the vehicle and another part (P2) of the capacity of the battery which can be used to supply an intelligent network (REI), in particular according to the prediction made by the prediction means and/or according to data external to the vehicle. Vehicle according to Claim 1, characterized in that the vehicle (VE) is a motor vehicle. Vehicle according to Claim 1 or 2, characterized in that the prediction means (PR) are able to calculate the subsequent availability of calories and/or cold temperatures, and/or the subsequent calorie and/or cold needs from at least one of the following input data:
- data related to the user's habits and/or comfort preferences, in particular:
y. data related to the temperature of the vehicle cabin usually desired by the user,
y. air flow usually desired by the user in the passenger compartment,
y. distribution of the air flow usually desired by the user in the passenger compartment, between different points of entry of the air into the passenger compartment, such as vents,
y. distribution between the fresh air outside the passenger compartment and the recycled air from the passenger compartment, usually desired by the user,
y. orientation of the aerators usually desired by the user,
- data related to the user's driving habits and/or preferences, in particular:
y. vehicle speed data,
y. vehicle driving time,
y. vehicle downtime,
y. vehicle acceleration,
y. speed of a heat or electric motor of the vehicle,
- data related to the user's journey, in particular:
y. geographical coordinates of the place of departure and/or the place of arrival planned or provided by the user,
y. real-time geographical coordinates of the vehicle,
y. meteorological data, such as the speed and direction of the wind, the temperature, the rainfall, the hygrometry, in particular on the route, the place of parking and/or the place of arrival planned,
y. traffic conditions on the route,
- data related to the state of charge of a battery, in particular:
y. battery charging type, such as fast charging or normal charging,
y. expected charging time. Electrical energy management system comprising:
- an intelligent electrical energy network (REI),
- a database (BD) comprising information relating to the electrical consumption of a plurality of vehicles,
- management means (GD) able to determine or receive information relating to the needs of the intelligent network, information relating to the electrical consumption of the vehicles, and to authorize the distribution of electrical energy between electrical energy sources of the vehicles connected to the smart grid and smart grid electrical energy receivers and/or between smart grid electrical energy sources and electrical energy receivers of vehicles connected to the smart grid,
the management means supplying information to vehicles according to one of Claims 1 to 3, so as to dynamically adjust the part (P1) of the battery (B) which is reserved for the energy consumer and for the motor of the vehicle and the part (P2) of the battery (B) which can be used to supply the intelligent network (REI), from the data coming from the database (BD). System according to Claim 4, characterized in that the management means (GD) comprise selection means able to select the vehicles intended to exchange electrical energy with the intelligent network, as a function in particular of the charge rate of the battery of each vehicle, the distribution between said portions and/or the state of health of the battery of each vehicle. System according to Claim 4 or 5, characterized in that the management means (GD) comprise prediction means aimed at determining a predicted model of the electrical consumption of the vehicle over a given period of use or between two consecutive recharges of the drums. System according to Claim 6, characterized in that the prediction means are capable of
- determine the cumulative capacity Cu of the vehicle, that is to say the accumulated energy used between two charging sessions, in several cases of use of the vehicle,
- optionally, determining at least one density estimate E(Cu) of the cumulative capacities,
- determining the predicted model on the basis of the cumulative capacity Cu and/or the density E(Cu). System according to one of Claims 4 to 7, characterized in that the management means (GD) include control means making it possible to control whether the use of the vehicle corresponds to the predicted model. System according to one of Claims 4 to 8, characterized in that the management means (GD) communicate with the vehicles via a telecommunications network. System according to one of Claims 4 to 9, characterized in that the database (BD) comprises information relating to the electrical consumption of at least one vehicle managed by the installation, as a function of at least one of the following parameters:
- the state of thermal load of a calorie and/or cold store of the vehicle,
the state of charge of the vehicle battery,
- the state of health of the vehicle battery,
- the vehicle user profile,
- the identity of the vehicle user,
- the type of vehicle,
- meteorological data linked to the vehicle,
- the date of use of the vehicle,
- the location of the vehicle,
- the traffic situation in the geographical area of the vehicle,
- the time habits of the vehicle user,
- the habits related to the comfort of the user of the vehicle,
- the state of charge of a calorie and/or cold store of the vehicle.