METHOD FOR OPTIMIZING THE ENERGY DISTRIBUTION OF A VEHICLE

Abstract

The invention relates to a method for optimizing the energy distribution of an electric or hybrid vehicle and to a vehicle implementing such a method. The method comprises the following steps: - a measurement step (E1) of a plurality of parameters recorded on a plurality of vehicle components by a plurality of sensors; - a calculation step (E2) of an energy distribution by applying a model predetermined by a test map to the plurality of measured parameters, the test map comprising a plurality of test values ​​for each parameter of the plurality of parameters and a plurality of energy distributions; - a power supply step (E3) of the plurality of components by a battery according to the calculated energy distribution so as to optimize the energy distribution of the electric or hybrid vehicle. Abstract figure: Fig. 1

Description

Title of the invention: METHOD FOR OPTIMIZING THE ENERGY DISTRIBUTION OF A VEHICLE

[0001] The invention relates to a method for optimizing the energy distribution of a vehicle. For the purposes of this invention, "energy distribution" means the distribution of fuel, hydrogen, electrical energy, electrical current delivered by the terminals of an electronic charger to an electric vehicle battery, or a combined distribution of several of these elements. The method is particularly suitable for electric or hybrid vehicles. For the purposes of this invention, "electric or hybrid vehicles" means vehicles equipped with at least one battery for storing electrical energy for propulsion. This includes vehicles with a single means of electric propulsion, as well as vehicles incorporating at least one mode of electric propulsion.

[0002] Patent application IN202321058001 describes a device for managing the energy consumption of an electric vehicle battery. The device includes a data collector configured to measure a plurality of parameters relating to vehicle operation, environmental conditions, and battery state. The device further includes an analysis means configured to implement a machine learning algorithm to calculate a command based on the plurality of measured parameters. The device also includes a control means configured to control the battery according to the calculated command. In this way, the battery's energy consumption is more efficient.

[0003] However, such a device is limited to the energy consumption of the battery without measuring parameters relating to other vehicle components. Thus, the contribution of this device to the overall energy efficiency of the vehicle is limited. Furthermore, this device is limited to electric vehicles, without taking into account existing vehicles, particularly those with an internal combustion engine.

[0004] The invention aims to overcome these drawbacks and proposes a method simple to implement on a vehicle to optimize the vehicle's energy distribution.

[0005] To achieve this objective, the invention proposes a method for optimizing the energy distribution of a vehicle comprising: - a plurality of components including at least one motor, battery or auxiliary component; - a plurality of sensors configured to measure a plurality of components, the plurality of sensors comprising at least one voltmeter, one ammeter, one thermometer or one accelerometer, the method comprising the following steps: - a measurement step of a plurality of parameters recorded on the plurality of components by the plurality of sensors; - a step of calculating an energy distribution by applying a model predetermined by a test map to the plurality of measured parameters, the test map including a plurality of test values ​​for each parameter of the plurality of parameters and a plurality of energy distributions; - a step of supplying the plurality of components according to the calculated energy distribution in order to optimize the energy distribution of the vehicle.

[0006] Such a process optimizes the energy distribution of a vehicle so that energy is dynamically allocated among a plurality of components, including an engine, a battery, and auxiliary components. The calculated energy distribution, through energy management, improves the vehicle's overall energy efficiency and extends battery life. When the vehicle needs recharging, the system interacts with smart charging stations, adjusting the process to reduce charging times while meeting the vehicle's energy requirements. Furthermore, by regularly measuring the plurality of components, the system reduces energy losses so that available resources are used more efficiently.This process also helps to limit battery wear and anticipate potential failures, thereby reducing downtime for maintenance and lowering both operating costs and technical interventions. Furthermore, the process is compatible with existing vehicle infrastructure and integrates easily into various vehicle models.

[0007] Advantageously, the method includes, in the calculation step, training a machine learning algorithm from the predetermined model and the energy distribution is calculated by applying the predetermined model comprising the trained machine learning algorithm.

[0008] Advantageously, the machine learning algorithm comprises a plurality of weight values, the training comprising the following sub-steps: - a sub-step of predicting an energy distribution from the plurality of test values ​​and the plurality of weights; - a sub-step for calculating an error value between the predicted energy distribution and the plurality of energy distributions in the test mapping; - a substep of updating the plurality of weight values ​​using the calculated error value.

[0009] Advantageously, the method includes, after the calculation step, an aggregation step of the calculated energy distribution and the plurality of measured parameters to the predetermined model, the energy distribution being recalculated by applying the aggregated model to the plurality of measured parameters, and, in the power supply step, the plurality of components is powered according to the recalculated energy distribution.

[0010] The invention also relates to a computer program comprising program code instructions for executing the steps of the process for energy distribution optimization defined as above, when said program is running on a computer.

[0011] The invention further relates to an assembly comprising an electrical circuit and an electronic control unit including acquisition means, processing means using software instructions stored in memory and control means configured for the implementation of the computer program defined as above.

[0012] The invention further relates to a vehicle comprising: - a plurality of components including at least one motor, battery or auxiliary component; - a plurality of sensors configured to measure the plurality of components, the plurality of sensors including at least one voltmeter, one ammeter, one thermometer or one accelerometer; - a set defined as above.

[0013] Thus, the invention provides for one or more sets of sets. When several sets are present, processing modules are distributed at different locations within the vehicle, so that each module supports specific subsystems such as the battery, the motor, or auxiliary systems. This configuration ensures greater flexibility in managing the plurality of measured parameters and the vehicle's energy distribution, so that the process can adapt to the specific needs of each component. In this way, when one set fails, the other sets ensure continuity of operations, guaranteeing the reliability of the process without compromising the improvement in the vehicle's energy efficiency.Furthermore, by reducing data processing latency, this distributed architecture also allows for a faster response to the needs of each component, improving the energy distribution of each component.

[0014] Advantageously, the vehicle comprises a single assembly.

[0015] Using a single unit to centralize the vehicle's energy distribution simplifies and streamlines data management and energy distribution. This centralization combines energy management functions into a single unit, facilitating integration into existing electric vehicles without requiring significant modifications to current vehicle architectures. This simplifies maintenance operations, as the configuration reduces the number of dispersed components, thus limiting technical interventions and the risk of errors. Therefore, when intervention or an update is necessary, a single unit provides quick and direct access to data and systems, enabling maintenance operations to be performed more efficiently and in less time.

[0016] Advantageously, the auxiliary component includes at least one means of air conditioning or one means of infotainment.

[0017] The invention further relates to a fleet of vehicles comprising: - a first vehicle defined as previously; - a second vehicle defined as previously; - a means of telecommunication connected to the first vehicle and the second vehicle.

[0018] This configuration offers the technical advantage of centralizing and optimizing data and energy flow management at the fleet level, so that vehicles can use and reuse the calculations of each vehicle's process. When the telecommunication method is cloud-based, vehicle data is transmitted to an external platform capable of processing large volumes of information and performing more in-depth analyses. This telecommunication method allows for updates to the process model, thereby improving energy distribution without requiring physical intervention on each vehicle, while ensuring the process adapts to the vehicles' operating conditions.Furthermore, the integration of a telecommunications system using the "Internet of Things" ensures, through connected sensors, direct interactions with the environment, such as smart charging stations, in order to improve energy distribution and increase the overall energy efficiency of the fleet.

[0019] The invention will be further detailed by describing non-limiting embodiments, and based on the attached figure illustrating a variant of the invention, in which: - [Fig. 1] illustrates a flowchart of a process for optimizing the energy distribution of a vehicle according to an embodiment of the invention.

[0020] Figure 1 schematically illustrates the steps described below for a method of optimizing the energy distribution of a vehicle. The vehicle comprises a plurality of components, including at least one motor, one battery, or one auxiliary component. The motor is preferably an electric motor. The vehicle also comprises a plurality of sensors configured to measure the plurality of components, the plurality of sensors including at least one voltmeter, one ammeter, one thermometer, or one accelerometer. Preferably, the auxiliary component includes at least one air conditioning or infotainment system.

[0021] In a measurement step E1, a plurality of parameters are recorded on the plurality of components by the plurality of sensors. Advantageously, the plurality of measured parameters includes a battery health status value, a battery charge cycle value, and a battery discharge cycle value.

[0022] In a computation step E2, an energy distribution is calculated by applying a model predetermined by a test map to the plurality of measured parameters. The test map comprises a plurality of test values ​​for each parameter of the plurality of parameters and a plurality of energy distributions. Advantageously, a machine learning algorithm is trained from the predetermined model, and the energy distribution is calculated by applying the predetermined model comprising the trained machine learning algorithm. Preferably, the machine learning algorithm is of the supervised learning type.

[0023] For example, the power distribution prioritizes the power supply to the motor over the auxiliary component when the battery is under load.

[0024] Even more advantageously, the machine learning algorithm comprises a plurality of weight values. The training includes the substeps described below.

[0025] In a prediction substep, an energy distribution is predicted from the plurality of trial values ​​and the plurality of weights.

[0026] In a calculation substep, an error value between the predicted energy distribution and the plurality of energy distributions of the test mapping.

[0027] In an update substep, the plurality of weight values ​​is updated using the calculated error value.

[0028] The process advantageously includes an aggregation step, the calculated energy distribution and the plurality of measured parameters being aggregated to the predetermined model, the energy distribution being recalculated by applying the aggregated model to the plurality of measured parameters.

[0029] In a power supply stage E3, the plurality of components is powered according to the calculated energy distribution so as to optimize the vehicle's energy distribution. Advantageously, the plurality of components is powered according to the recalculated energy distribution.

[0030] The method advantageously includes an emission step alerting the driver of the calculated energy distribution.

[0031] According to the invention, a fleet of vehicles comprises a first vehicle, a second vehicle, and a telecommunications device. The telecommunications device is connected to the first vehicle and the second vehicle.

Claims

Demands

1. A method for optimizing the energy distribution of a vehicle comprising: - a plurality of components including at least one motor, one battery, or one auxiliary component; - a plurality of sensors configured to measure the plurality of components, the plurality of sensors including at least one voltmeter, one ammeter, one thermometer, or one accelerometer, the method comprising the following steps: - a measurement step (E1) of a plurality of parameters taken from the plurality of components by the plurality of sensors; - a calculation step (E2) of an energy distribution by applying a model predetermined by a test map to the plurality of measured parameters, the test map including a plurality of test values ​​for each parameter of the plurality of parameters and a plurality of energy distributions;- a power supply stage (E3) of the plurality of components according to the energy distribution calculated in order to optimize the energy distribution of the vehicle.;

2. A method according to claim 1, characterized in that the method comprises, in the calculation step, training a machine learning algorithm from the predetermined model and the energy distribution is calculated by applying the predetermined model comprising the trained machine learning algorithm.

3. A method according to claim 2, characterized in that the machine learning algorithm comprises a plurality of weight values, the training comprising the following substeps: - a substep of predicting an energy distribution from the plurality of test values ​​and the plurality of weights; - a substep of calculating an error value between the predicted energy distribution and the plurality of energy distributions from the test mapping; - a substep of updating the plurality of weight values ​​using the calculated error value.

4. A method according to any one of claims 1 to 3, characterized in that the method comprises, after the calculation step, a step aggregation of the calculated energy distribution and the plurality of measured parameters to the predetermined model, the energy distribution being recalculated by applying the aggregated model to the plurality of measured parameters, and, at the power-up stage, the plurality of components is powered according to the recalculated energy distribution.

5. Computer program comprising program code instructions for performing the steps of the process for energy distribution optimization according to any one of claims 1 to 4, when said program is running on a computer.

6. Assembly comprising an electrical circuit and an electronic control unit including acquisition means, means for processing by software instructions stored in memory and control means configured for the implementation of the computer program according to claim 5.

7. Vehicle comprising: - a plurality of components including at least one motor, one battery or one auxiliary component; - a plurality of sensors configured to measure the plurality of components, the plurality of sensors including at least one voltmeter, one ammeter, one thermometer or one accelerometer; - an assembly according to claim 6.

8. Vehicle according to claim 7, characterized in that the vehicle comprises a single assembly.

9. Vehicle according to claim 7 or 8, characterized in that the auxiliary component comprises at least one air conditioning means or one infotainment means.

10. Fleet of vehicles comprising: - a first vehicle according to any one of claims 7 to 9; - a second vehicle according to any one of claims 7 to 9; - a means of telecommunication connected to the first vehicle and the second vehicle.

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