Description:FIELD OF THE INVENTION

[0001] The present invention relates to a hybrid energy optimization system for vehicles that is capable of managing use of electrical and fuel energy during vehicle operation to continuously balance both energy sources in real time in order to improve overall driving efficiency, reduce unnecessary energy loss, and minimize the need for manual intervention by the driver.

BACKGROUND OF THE INVENTION

[0002] Hybrid vehicles rely on both fuel and electric energy sources, offering improved efficiency and reduced emissions, yet managing the optimal use of these energy sources remains a challenge for drivers, especially under varying traffic, terrain, and weather conditions. Drivers often struggle to balance energy consumption between the battery and fuel engine, leading to suboptimal fuel efficiency or premature battery depletion. Traditional vehicles lack real-time guidance for energy allocation, forcing users to rely on experience and manual adjustments, which results in increased energy waste, reduced driving range, and higher operational costs. Therefore, there is a growing need for a system that optimized energy usage and continuously monitors performance, predicts energy demands, and provides actionable feedback to optimize energy usage efficiently and reliably.

[0003] Several hybrid and electric vehicle energy management systems are available in the market, including conventional battery management systems, engine control units, and eco-driving assist features. While these systems monitor basic energy usage and provide limited guidance, they often fail to dynamically optimize energy distribution between the battery and fuel engine based on real-time driving conditions, traffic patterns, terrain, or weather. Many systems lack predictive capabilities and cannot anticipate energy demands, leading to inefficient consumption and reduced driving range. Furthermore, existing solutions provide minimal feedback to the driver, do not integrate multiple environmental inputs effectively, and are often unable to issue actionable alerts for corrective energy-saving measures, limiting overall efficiency.

[0004] US7360615B2 discloses a predictive energy management system for a hybrid vehicle that uses certain vehicle information, such as present location, time, 3-D maps and driving history, to determine engine and motor power commands. The system forecasts a driving cycle profile and calculates a driver power demand for a series of N samples based on a predetermined length of time, adaptive learning, etc. The system generates the optimal engine and motor power commands for each N sample based on the minimization of a cost function under constraint equations. The constraint equations may include a battery charge power limit, a battery discharge power limit, whether the battery state of charge is less than a predetermined maximum value, whether the battery state of charge is greater than a predetermined minimum value, motor power output and engine performance. The system defines the cost function as the sum of the total weighted predicted fuel consumed for each sample. The system then selects the motor and engine power commands for the current sample.

[0005] CN202703576U discloses a hybrid vehicle energy management system, which comprises a hybrid vehicle control unit HCU, an engine control unit EMS, a motor control unit MCU, a mechanical automatic transmission control unit TCU, an automatic clutch control unit ECA, a battery Control unit BMS, brake control unit BCU7, all control units are connected through CAN bus and communicate through CAN bus. Wherein the vehicle control unit HCU of the hybrid electric vehicle sends to the remote server through the GPRS module the state parameters required by the energy management strategy on the target line formulated by the remote server, and receives the relevant energy management information sent by the remote server through the GPRS module calibration parameters.

[0006] Conventionally, many systems are available in the market for optimizing energy usage of hybrid vehicles. However, the cited inventions lack to provide the ability to dynamically manage energy distribution between the battery and fuel engine in real time, fail to integrate multiple environmental inputs such as traffic, terrain, and weather, and do not provide predictive analytics or actionable feedback to the driver, resulting in inefficient energy usage, reduced driving range, and limited operational reliability.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that requires to be capable of monitoring energy performance, predict future energy demands based on driving patterns and real-time conditions, and automatically optimizes energy allocation between fuel and electric sources while providing real-time alerts and guidance to the driver for enhanced efficiency, reduced energy waste, and improved vehicle performance under all driving scenarios.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that automatically manages electrical and fuel energy usage for improved efficiency during vehicle operation.

[0010] Another object of the present invention is to develop a system that enable the prediction of future energy demands using past driving behavior and real-time travel information for better range management.

[0011] Yet another object of the present invention is to develop a system that monitor overall energy performance continuously and issue alerts for inefficient usage or suggested corrective actions.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a hybrid energy optimization system for vehicles that manages electrical and fuel energy usage during vehicle operation and predicted future energy demands using past driving behavior and real-time travel information, that allows better management of energy range.

[0014] According to an embodiment of the present invention, a hybrid energy optimization system for vehicles, includes a GPS-based location tracking module integrated with IoT sensors adapted to collect real-time driving data including traffic density, terrain, and weather conditions, an energy management controller operatively coupled with the GPS-based location tracking module and configured to dynamically adjust energy consumption between the vehicle battery and fuel assembly to maximize efficiency and automatically distribute energy based on real-time location, traffic, and weather, a terrain-sensitive module operatively coupled with the energy management controller to sense uphill or downhill road conditions and adjust regenerative braking and energy flow to improve power recovery and battery utilization, the energy management controller further configured to flexibly adjust energy allocation between the vehicle battery, drivetrain, and auxiliary systems based on ambient temperature and humidity, the GPS-based tracking module and IoT sensors collaborating with the energy management controller to optimize energy flow during stop-and-go urban traffic and long-distance rural travel.

[0015] According to another embodiment of the present invention, the system further includes an analytical energy allocation module configured with predictive protocols to anticipate changes in driving conditions and regulate energy usage in advance to optimize driving range, the analytical energy allocation module further configured with artificial intelligence protocols to learn from historical driving data and driving habits in order to predict patterns and adjust energy dispatch accordingly, while also integrating weather forecast data with GPS-based route data to prevent unnecessary depletion of stored energy, a cloud-based monitoring module communicatively coupled with the energy management controller to provide real-time data analytics, energy performance tracking, and user-specific consumption reports and enable remote access to vehicle performance data, and the energy management controller generating real-time alerts and feedback to the driver, including suggested energy-saving actions derived from traffic and weather analysis, to optimize energy consumption during operation.

[0016] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a schematic representation of a block diagram depicting a workflow of a hybrid energy optimization system for vehicles.

DETAILED DESCRIPTION OF THE INVENTION

[0018] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0019] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0020] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0021] The present invention relates to a hybrid energy optimization system for vehicles that automatically manages electrical and fuel energy usage during vehicle operation to improve efficiency, and continuously monitors overall energy performance and issues alerts whenever inefficient usage is detected, offering corrective actions to help optimize energy consumption.

[0022] Referring to Figure 1, a schematic representation of a block diagram depicting a workflow of a hybrid energy optimization system for vehicles. The system disclosed in the present invention includes a GPS-based location tracking module that works with various IoT sensors to gather real-time information during vehicle operation. The real-time information includes traffic density, road terrain, and current weather conditions along the travel path. By continuously collecting this information, the system gains a full understanding of the environment in which the vehicle is operating. This real-time data allows an inbuilt microcontroller associated with the system to make smarter decisions about energy management, route performance, and overall vehicle efficiency under changing conditions.

[0023] The GPS-based location tracking module receives signals from multiple satellites in the GPS constellation. Each satellite transmits a signal that includes its position and the precise time of the signal. The GPS-based location tracking module uses these signals to calculate the distance from each satellite based on the time taken by the signal to reach the GPS-based location tracking module. By receiving signals from multiple satellites, the GPS-based location tracking module perform trilateration and calculates the exact position (latitude, longitude, and altitude) of the vehicle. The GPS-based tracking module and IoT sensors collaboratively operate with the energy management controller to optimize energy flow in varying traffic patterns, including stop-and-go urban driving and long-distance rural travel.

[0024] An energy management controller is operatively coupled with the GPS-based location tracking module to dynamically adjust energy consumption between the vehicle battery and fuel assembly in order to maximize efficiency. The microcontroller initiates analysis of location, traffic, and weather data to ensure energy is optimally allocated. The energy management controller also flexibly manages energy distribution between the vehicle battery, drivetrain, and auxiliary systems based on real-time weather parameters such as ambient temperature and humidity.

[0025] A terrain-sensitive module is operatively connected to the energy management controller and adapted to sense uphill or downhill road conditions, adjusting regenerative braking and energy flow accordingly to improve power recovery and battery utilization. The terrain-sensitive module consists of an inclinometer integrated within a sensor housing connected to the vehicle’s chassis or suspension. The terrain-sensitive module continuously measures the angle of the road surface relative to the horizontal plane using accelerometer-based detection. When the module detects an uphill or downhill slope, it sends a signal to the energy management controller.

[0026] During uphill motion, the energy management controller adjusts power output and engages additional propulsion or regenerative settings to compensate for increased load. During downhill motion, the energy management controller increases regenerative braking to recover energy and reduce battery drain.

[0027] A cloud-based monitoring module is communicatively coupled with the energy management controller to provide real-time data analytics, energy performance tracking, and user-specific consumption reports, and monitoring module allows for remote access to data such as regenerative braking efficiency, battery health, and overall energy usage for predictive maintenance.

[0028] The cloud-based monitoring module operates by continuously receiving energy performance data from the energy management controller, including real-time metrics on battery charge, fuel consumption, and regenerative braking efficiency. The cloud-based monitoring module processes and stores this data on a remote cloud server, enabling comprehensive analytics and visualization of vehicle energy usage. The cloud-based monitoring module generates user-specific consumption reports, highlighting energy trends, deviations, and areas for optimization. Through secure connectivity, authorized users remotely access this information via a computing unit, allowing monitoring of battery health, energy distribution, and overall efficiency.

[0029] A communication module is connected to the computing unit to transmit real-time data analytics, enabling remote viewing of energy performance, driving metrics, and efficiency updates. The computing unit mentioned herein includes, but not limited to smartphone, laptop, tablet.

[0030] The communication module mentioned herein includes, but not limited to Wi-Fi (Wireless Fidelity) module, Bluetooth module, GSM (Global System for Mobile Communication) module. The communication module used in the system is preferably the Wi-Fi module. The Wi-Fi module enables wireless communication by transmitting and receiving data over radio frequencies using IEEE 802.11 protocols. It connects to a network via an access point, converting digital data into radio signals. The module processes TCP/IP protocols for data exchange, interfaces with microcontrollers through UART/SPI, and ensures encrypted communication using WPA/WPA2 security standards for secure and efficient wireless connectivity.

[0031] An analytical energy allocation module uses predictive protocols to evaluate current sensor data and past driving trends in order to foresee upcoming changes in traffic, terrain, or weather. With this foresight, the energy allocation module adjusts how energy will be used before those conditions actually occur. By regulating consumption in advance, the module helps extend the vehicle’s driving range and maintain efficient energy balance throughout the trip.

[0032] The Predictive protocol operates by analyzing historical driving data, real-time sensor inputs, and external information such as traffic patterns and weather forecasts. The energy allocation module collects past behavior like speed variations, frequent stop locations, and typical energy usage. Using machine learning protocols, the predictive protocols identify patterns and trends, allowing the energy allocation module to forecast upcoming energy demands.

[0033] For example, if a route usually involves heavy traffic at certain times or steep climbs ahead, the predictive protocols anticipate higher energy consumption. The protocols then inform the energy management controller to reserve or redistribute energy accordingly, promoting smoother transitions, reduced wastage, and better optimization of battery and fuel usage before actual driving conditions change.

[0034] The energy allocation module is further configured with artificial intelligence protocols to learn from historical driving data and driver habits, allowing the energy allocation module to adapt and predict future driving patterns and adjust energy dispatch accordingly.

[0035] The artificial intelligence protocols work by collecting large sets of driving data over time, including speed patterns, energy consumption, route selections, and driver behavior. Using machine learning protocols, the system processes this information to identify consistent patterns and performance trends. The artificial intelligence protocols then build predictive models that estimate future energy demands based on similar driving scenarios encountered in the past. The predictive models allow the system to automatically adapt energy allocation strategies in real time for better efficiency.

[0036] The analytical energy allocation module is also enabled to integrate weather forecast data with GPS-based route data to anticipate environmental changes and prevent unnecessary depletion of stored energy. The analytical energy allocation module integrates weather forecast data with GPS-based route information to determine future environmental conditions such as rain, temperature drops, or strong winds along the vehicle’s path. By matching predicted weather with the planned route, it anticipates how these conditions might affect energy consumption.

[0037] For example, colder weather may reduce battery efficiency or require extra energy for heating, while headwinds or rain may increase resistance and fuel demand. Using this forecasted data in advance, the module adjusts energy usage and reserves, ensuring the system does not deplete stored energy unnecessarily and maintains sufficient levels for upcoming conditions.

[0038] The energy management controller continuously evaluates driving behavior and compares real-time energy usage with predefined efficiency thresholds. In case, the energy management controller detects excessive power consumption or inefficient practices, an immediately alert is transmitted to the driver through visual or audio notifications through the infotainment cluster or speaker of the vehicle. The energy management controller also analyzes traffic and weather conditions to suggest actions such as reducing acceleration, switching driving mode, or conserving battery usage. By providing these real-time recommendations, the system guides the driver toward optimal driving habits and helps prevent unnecessary energy waste during operation.

[0039] The system herein improves battery lifespan by intelligently reducing unnecessary charge-discharge cycles and optimizing the overall energy flow between electric and fuel sources. By carefully managing when and how energy is drawn from or supplied to the battery, it prevents overuse, overheating, and excessive strain, which are major causes of degradation.

[0040] This optimization ensures that both batteries maintain their capacity and efficiency for longer periods, reducing the frequency of replacements and lowering maintenance costs. Additionally, the system’s adaptive intelligence allows the system to adjust energy usage dynamically based on real-world driving conditions, ensuring sustained performance, improved efficiency, and enhanced sustainability for hybrid and electric vehicles over time.

[0041] In an exemplary embodiment of the present invention, a hybrid electric delivery van is operating in a city with mixed traffic conditions and varying terrains. As the van navigates stop-and-go urban streets and uphill or downhill roads, the system dynamically manages the energy flow between the battery and fuel engine. The predictive protocols anticipate energy demands based on traffic patterns, route, and weather forecasts, while the artificial intelligence protocols learn the driver’s habits to optimize energy dispatch. The terrain-sensitive module adjusts regenerative braking on slopes, and the cloud-based monitoring provides real-time performance data and alerts. This ensures maximum driving range, improved fuel efficiency, reduced battery wear, and actionable feedback for the driver during daily operations.

[0042] The present invention works best in the following manner, where the GPS-based location tracking module integrated with IoT sensors continuously collects real-time driving data such as traffic density, terrain, and weather conditions. This data is sent to the energy management controller, which dynamically adjusts energy consumption between the vehicle battery and fuel assembly to maximize efficiency and automatically distributes energy according to location, traffic, and weather conditions. The terrain-sensitive module senses uphill or downhill road conditions and works with the energy management controller to adjust regenerative braking and power recovery. The energy management controller also flexibly manages energy allocation among the vehicle battery, drivetrain, and auxiliary systems based on real-time weather parameters including ambient temperature and humidity. The GPS-based tracking module and IoT sensors collaborate with the energy management controller to optimize energy flow in both stop-and-go urban traffic and long-distance rural travel.

[0043] In continuation, the analytical energy allocation module configured with predictive protocols anticipates changes in driving conditions and regulates energy usage in advance to optimize driving range while learning from historical driving data and habits to adjust energy dispatch. This analytical energy allocation module further integrates weather forecast data with GPS-based route data to anticipate environmental changes and prevent unnecessary depletion of stored energy. The cloud-based monitoring module communicates with the energy management controller to provide real-time data analytics, performance tracking, and user-specific consumption reports, enabling remote access to vehicle performance data such as regenerative braking efficiency and battery health. The energy management controller generates real-time alerts and feedback to the driver regarding optimum driving practices or excessive energy usage beyond predefined thresholds and provides suggested energy-saving actions derived from traffic and weather analysis, ensuring efficient and adaptive energy consumption during operation.

[0044] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:We Claim:

1) A hybrid energy optimization system for vehicles, comprising:

i) a GPS-based location tracking module integrated with IoT sensors, adapted to collect real-time driving data including traffic density, terrain, and weather conditions;

ii) an energy management controller operatively coupled with the GPS-based location tracking module, the controller configured to dynamically adjust energy consumption between the vehicle battery and fuel assembly to maximize efficiency;

iii) an analytical energy allocation module configured with predictive protocols to anticipate changes in driving conditions and regulate energy usage in prior to optimize driving range; and

iv) a cloud-based monitoring module communicatively coupled with the energy management controller to provide real-time data analytics, energy performance tracking, and user-specific consumption reports.

2) The system as claimed in claim 1, wherein the energy management controller automatically adjusts energy distribution between the vehicle battery and fuel assembly based on location, traffic, and weather conditions.

3) The system as claimed in claim 1, wherein a terrain-sensitive module is operatively coupled with the energy management controller, the terrain-sensitive module adapted to sense uphill or downhill road conditions and adjust regenerative braking and energy flow to improve power recovery and battery utilization.

4) The system as claimed in claim 1 and 2, wherein the energy management controller is further configured to flexibly adjust energy allocation between the vehicle battery, drivetrain, and auxiliary systems based on real-time weather parameters including ambient temperature and humidity.

5) The system as claimed in claim 1, wherein the GPS-based tracking module and IoT sensors collaboratively operate with the energy management controller to optimize energy flow in varying traffic patterns including stop-and-go conditions in urban regions and long-distance travel in rural regions.

6) The system as claimed in claim 1, wherein the analytical energy allocation module is configured with artificial intelligence protocols to learn from historical driving data and driving habits, thereby predicting driving patterns and adjusting energy dispatch accordingly.

7) The system as claimed in claim 2 and 4, wherein the energy management controller is configured to generate real-time alerts to notify the driver of optimum driving practices or excessive energy usage beyond a predefined threshold.

8) The system as claimed in claim 1, wherein the cloud-based monitoring module enables remote access to vehicle performance data including regenerative braking efficiency, battery health, and overall energy usage for predictive maintenance and decision-making.

9) The system as claimed in claim 1 and 6, wherein the analytical energy allocation module is further configured to integrate weather forecast data with GPS-based route data to anticipate environmental changes and prevent unnecessary depletion of stored energy.

10) The system as claimed in claim 4 and 7, wherein the energy management controller is configured to provide real-time driver feedback, including suggested energy-saving actions derived from analysis of traffic and weather data, thereby enabling the driver to optimize energy consumption during operation.