Description:FIELD OF THE INVENTION

[0001] The present invention relates to a multi-source energy management system for hybrid electric vehicles (HEVs) that is capable of dynamically harvesting, distributing, and optimizing energy from multiple sources in real time, ensuring efficient power utilization, improved vehicle performance, extended battery life, and enhanced driving range.

BACKGROUND OF THE INVENTION

[0002] In modern hybrid electric vehicles (HEVs), efficient energy management is critical to maximize driving range, reduce energy consumption, and maintain battery health. HEVs rely on multiple energy sources, including regenerative braking, hydrogen fuel cells, and solar panels, each with variable availability and output. Users face challenges in ensuring seamless power supply under fluctuating driving conditions, such as varying terrain, traffic density, load, and weather, which leads to inefficient energy utilization, battery stress, or unexpected power shortages. Traditional energy management means often fail to dynamically prioritize among multiple sources or predict future energy demands, highlighting the need for an adaptive, multi-source energy management means capable of real-time optimization and predictive control to ensure sustained vehicle performance.

[0003] Traditional energy management means in hybrid electric vehicles (HEVs) primarily rely on fixed-rule or single-source strategies, such as simple battery management, regenerative braking control, or standalone fuel cell integration. These means often operate with limited adaptability, lacking real-time assessment of vehicle load, terrain, driving patterns, or environmental conditions. As a result, energy distribution is suboptimal, leading to inefficient utilization of available resources, reduced driving range, and accelerated battery degradation. Moreover, traditional means rarely integrate predictive insights from external factors like weather, traffic, or solar availability, making them unable to proactively manage energy demands. Consequently, users experience inconsistent vehicle performance, higher energy wastage, and limited capability to leverage multi-source energy efficiently under dynamic driving conditions.

[0004] CN112319462A discloses about an energy management method for a plug-in hybrid electric vehicle, and belongs to the technical field of new energy vehicle control. Firstly, establishing a power battery life prediction model by using battery historical operation data, determining the current battery life of a vehicle based on the model, and establishing a power split type PHEV model and an equivalent life cycle cost model; and then, solving to obtain the torque of the engine and the motor through an MPC system control model by taking the minimum life cycle cost as an optimization target and taking the minimum life cycle cost as a cost function, so as to realize the energy management of the plug-in hybrid electric vehicle. The invention aims at minimizing the whole vehicle life cycle cost, considers the influence of battery charge and discharge on the battery life, balances the contradiction between fuel economy and battery replacement, reduces the use cost and provides a new direction for energy management.

[0005] US20030197489A1 discloses about an apparatus for providing electrical power for a hybrid vehicle includes an integrated starter-generator and an inverter connected to an electrical energy storage device such as a capacitor and a battery by first and second switches respectively. A control turns the switches on and off in accordance with selected modes of operation to provide power from the capacitor to the integrated starter-generator and the battery and to provide power from the integrated starter-generator to the capacitor and the battery.

[0006] Conventionally, many systems available in market for hybrid electric vehicles provide limited adaptability, focus on single-source energy management, and lack predictive capabilities, resulting in suboptimal energy utilization, reduced driving range, increased battery stress, and inability to efficiently coordinate multiple energy sources under dynamic driving and environmental conditions.

[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 dynamically managing and optimizing multiple energy sources in hybrid electric vehicles, providing real-time adaptive control, predictive energy allocation, efficient battery utilization, and sustained vehicle performance under varying driving and environmental conditions.

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 is capable of managing and distributing energy from multiple sources, such as regenerative braking module, hydrogen fuel cell generator, and solar panels in a hybrid electric vehicle efficiently, ensuring optimal utilization of available energy under varying driving conditions.

[0010] Another object of the present invention is to develop a system that is capable of maximizing energy recovery from all sources while minimizing wastage, maintaining battery health, and supporting consistent vehicle performance during different driving and environmental scenarios.

[0011] Another object of the present invention is to develop a system that is capable of dynamically adapting energy allocation based on real-time driving conditions, vehicle load, terrain, and environmental factors, ensuring the most efficient use of available energy at all times.

[0012] Yet, another object of the present invention is to develop a system that is capable of continuously monitoring energy usage and provide predictive insights, enabling real-time optimization of energy flow and improving overall vehicle efficiency, driving range, and operational reliability.

[0013] 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

[0014] The present invention relates to a multi-source energy management system for hybrid electric vehicles (HEVs) that is capable of efficiently harvesting, distributing, and optimizing energy from multiple sources in real time, enhancing vehicle performance, extending battery life, and improving overall energy efficiency.

[0015] According to an aspect of the present invention, a multi-source energy management system for hybrid electric vehicles (HEVs) is comprising, a regenerative braking module constructed to harvest energy during braking of the HEV, a hydrogen fuel cell generator for supplying ecofriendly electrical power, a plurality of solar panels integrated into the HEV for converting sunlight into usable energy, a Neuro-adaptive Energy Optimization Module (NAEOM) for dynamically regulating and optimizing energy distribution among the available sources, and a cloud-based energy monitoring module for remote tracking, analysis, and predictive management of energy usage to ensure efficient vehicle operation and sustained battery health.

[0016] The system is further comprises, integrated sensors to monitor vehicle load, terrain, and driving patterns, enables the NAEOM to determine an efficient energy source out of the multiple energy sources for driving the HEV at any moment by employing spiking neural networks (SNNs) to improve energy flow, a communicatively coupled user interface installed on a computing unit displays energy distribution, battery health, and vehicle efficiency metrics.

[0017] 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

[0018] 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 block diagram of a multi-source energy management system for hybrid electric vehicles (HEVs).

DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] 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.

[0021] 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.

[0022] The present invention relates to a multi-source energy management system for hybrid electric vehicles (HEVs) that manages and optimizes power from various sources for hybrid electric vehicles. The system uses real-time data to dynamically distribute energy for powering the drive train and battery charging, enhancing efficiency and extending vehicle as well as battery life through a predictive, neuro-adaptive control module.

[0023] Referring to Figure 1, a block diagram of a multi-source energy management system for hybrid electric vehicles (HEVs) is illustrated, comprising a regenerative braking module 101, a hydrogen fuel cell generator 102, a plurality of solar panels 103, a Neuro-adaptive energy optimization module (NAEOM) 104, integrated sensors 105, a battery 106 of the HEV, and a cloud-based energy monitoring module 107.

[0024] The system disclosed herein includes a Neuro-Adaptive Energy Optimization Module (NAEOM) 104 configured to regulate and optimize distribution of energy among multiple available sources in the hybrid electric vehicle (HEV). The NAEOM 104 comprises a neuromorphic processor fed by an array of sensors. The neuromorphic processor employs spiking neural networks (SNNs) and predictive artificial intelligence (AI) models to analyze real-time driving parameters by employing an array of sensors to monitor parameters including vehicle load, terrain conditions, traffic density, driver behavior, and battery 106 charge status. Based on the continuous analysis, the NAEOM 104 dynamically allocates power from the regenerative braking module 101 of HEVs, hydrogen fuel cell generator 102, and integrated solar panels 103, ensuring that the most efficient energy source is prioritized under prevailing conditions. The Neuro-adaptive energy optimization module (NAEOM) 104 is further configured to learn and adapt over time by referencing historical energy usage patterns, thereby refining its predictive capabilities and continuously improving overall system efficiency. In addition, the NAEOM 104 interacts with the cloud-based energy monitoring module 107 to incorporate external inputs such as weather forecasts, solar availability, and traffic updates, thereby enabling proactive decision-making for future energy demands. By intelligently balancing short-term performance requirements with long-term battery 106 health and energy sustainability, the NAEOM 104 provides improved driving range, reduced energy wastage, and optimized performance of the HEV.

[0025] The system disclosed herein further comprises a network of integrated vehicle sensors 105, including but not limited to speed sensors, accelerometers, inclinometers, load sensors, and battery 106 management system (BMS) sensors, configured to continuously detect operational parameters such as vehicle speed, terrain gradient, vehicle load, acceleration demand, and battery 106 state of charge (SoC). Each sensor is operatively coupled to the NAEOM 104 via a controller area network (CAN) or other vehicle communication bus, transmitting real-time analog or digital signals corresponding to the measured parameter.

[0026] The speed sensors, implemented as wheel-mounted magnetic or optical encoders, operate by generating electrical pulses corresponding to the rotational motion of the wheels, which are then processed to determine real-time vehicle speed. The accelerometers, based on piezoelectric, capacitive, or MEMS technology, detect linear and lateral accelerations by converting mechanical forces into proportional electrical signals, enabling measurement of dynamic vehicle motion. The inclinometers, often employing MEMS gyroscopes or electrolytic tilt sensors, measure angular displacement relative to the horizontal plane, allowing detection of terrain slope and vehicle tilt. The load sensors, such as strain gauges or piezoelectric load cells, measure forces applied to the vehicle structure or suspension due to passenger and cargo weight, producing electrical signals proportional to the applied load. The BMS sensors continuously monitor the battery’s electrical parameters, including voltage, current, temperature, and SoC, using voltage dividers, current shunts, and temperature sensing circuits, thereby providing accurate assessment of battery 106 condition. All sensor data is transmitted via a vehicle communication bus, includes but not limited to, such as a Controller Area Network (CAN), Local Interconnect Network (LIN), or FlexRay, to the NAEOM 104, where the neuromorphic processor applies predictive protocols and spiking neural networks to determine optimal energy distribution among regenerative braking, hydrogen fuel cells, and solar panels 103, ensuring efficient energy utilization, enhanced battery 106 life, and improved overall vehicle performance.

[0027] During vehicle motion, the hybrid electric vehicle (HEV) continuously harvests and generates energy from multiple sources to ensure optimal power supply and efficiency. The solar panels 103 integrated into the hybrid electric vehicle (HEV) comprise photovoltaic (PV) cells that convert incident sunlight into electrical energy through the photovoltaic effect. Each PV cell generates a voltage when exposed to light, producing a current proportional to sunlight intensity and angle of incidence. The solar panels 103 are equipped with monitoring units continuously measure sunlight intensity, incident angle, and panel temperature. These measurements are converted into electrical signals and transmitted to the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 via the communication bus. The NAEOM 104 employs predictive artificial intelligence (AI) protocols to estimate short-term solar energy availability based on the collected sensor data, environmental conditions, and weather forecasts obtained from the cloud-based energy monitoring module 107. Based on these predictions, the NAEOM 104 dynamically adjusts battery 106 charging, vehicle power distribution, from solar energy in real time, taking into account vehicle demand and operational requirements. Power generated by the solar panels 103 is conditioned through a DC-DC converter to ensure voltage and current compatibility with the vehicle’s battery 106 system and auxiliary loads. By integrating real-time monitoring with predictive AI-based control, the solar panels 103 operate in coordination with the regenerative braking module 101 and hydrogen fuel cell generator 102 to optimize energy efficiency, maintain battery 106 health, and support sustained HEV operation under varying sunlight and driving conditions.

[0028] In an embodiment of the present invention, the monitoring units mentioned above, includes but not limited to, such as irradiance meters, inclinometer-based tilt sensors, and thermocouples or RTD-based temperature sensors, configured to continuously measure sunlight intensity, panel tilt, and temperature. The irradiance meters operate by capturing incident solar radiation using photodiodes or pyranometers, which convert light intensity into proportional electrical signals suitable for real-time measurement of available solar power. Inclinometer-based tilt sensors determine the angular orientation of the solar panels 103 relative to the horizontal plane, utilizing MEMS gyroscopes or electrolytic tilt elements that generate voltage outputs corresponding to panel inclination. Thermocouples, consisting of two dissimilar metals joined at one end, produce a voltage proportional to the temperature difference between the junction and reference, while RTD-based sensors vary electrical resistance in response to temperature changes, providing precise thermal measurements. The electrical signals generated by all these monitoring units are transmitted via the vehicle communication bus to the Neuro-Adaptive Energy Optimization Module (NAEOM) 104. The NAEOM 104 processes this data in real time using predictive artificial intelligence protocols to estimate short-term solar energy availability, optimize battery 106 charging, and adjust energy allocation for powering the drive train and charging the battery 106 , thereby enhancing overall energy efficiency, maintaining battery 106 health, and ensuring sustained vehicle operation under varying environmental conditions.

[0029] The regenerative braking module 101 of the hybrid electric vehicle (HEV) is configured to harvest kinetic energy during braking events and convert it into electrical energy for storage or immediate use. The regenerative braking module 101 includes, but not limited to, such as an electric traction motor, a braking actuator, a power electronics converter, and a controller unit. During braking, the braking actuator signals the controller to switch the traction motor into generator mode, causing the wheels’ rotational kinetic energy to be converted into electrical energy. The power electronics converter regulates the voltage and current generated by the motor to match the battery 106 requirements, ensuring safe and efficient energy transfer. Integrated wheel speed sensors, accelerometers, and brake pressure sensors provide real-time feedback to the controller, enabling modulation of regenerative braking force while maintaining vehicle safety and driver comfort. The harvested energy is immediately redirected for propulsion assistance, stored in the battery 106, or, if applicable, converted for hydrogen storage. The controller communicates continuously with the Neuro-Adaptive Energy Optimization Module (NAEOM) 104, which dynamically determines the optimal use of regenerated energy based on vehicle speed, load, terrain, battery 106 state of charge, and predicted driving conditions. By integrating real-time sensing, energy conversion, and predictive control, the regenerative braking module 101 enhances overall vehicle efficiency, reduces energy wastage, and contributes to extended battery 106 life.

[0030] The hydrogen fuel cell generator 102 of the hybrid electric vehicle (HEV) is configured to supply stable electrical power by converting chemical energy stored in hydrogen into electricity through an electrochemical reaction with oxygen. The hydrogen fuel cell generator 102, includes but not limited to, such as a hydrogen storage tank, fuel cell stack, hydrogen and air flow control valves, pressure and temperature sensors, and a power conditioning unit. Hydrogen from the storage tank is supplied to the anode of the fuel cell stack through flow control valves, while ambient air is supplied to the cathode. In the fuel cell stack, hydrogen molecules are split into protons and electrons; the electrons flow through an external circuit to generate electricity, while protons pass through an electrolyte membrane to combine with oxygen at the cathode, producing water as a byproduct. Pressure, temperature, and hydrogen flow sensors continuously monitor operating conditions to ensure safe and efficient energy generation, and the power conditioning unit regulates voltage and current to match the battery 106 requirements. The generator 102 communicates with the Neuro-Adaptive Energy Optimization Module (NAEOM) 104, which monitors real-time energy demand, battery 106 state of charge, and driving conditions to dynamically determine when to activate the fuel cell generator 102, thereby maintaining a stable baseline power supply and complementing energy from regenerative braking and solar panels 103. Through integration of real-time monitoring, energy conversion, and predictive control, the hydrogen fuel cell generator 102 enhances overall vehicle efficiency, ensures uninterrupted power availability, and supports extended driving range of the HEV.

[0031] The Neuro-Adaptive Energy Optimization Module (NAEOM) 104 employs spiking neural networks (SNNs) and predictive artificial intelligence (AI) protocols to dynamically evaluate real-time driving conditions, vehicle load, terrain characteristics, and environmental inputs. Data from integrated sensors 107—including speed sensors, accelerometers, inclinometers, load sensors, and battery 106 management system (BMS) sensors—are continuously transmitted to the NAEOM 104, while predictive inputs such as weather forecasts, sunlight availability, and traffic conditions are obtained from the cloud-based energy monitoring module 107. Based on this combined dataset, the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 determines optimal allocation of available energy sources to maximize vehicle efficiency, maintain battery 106 health, and ensure stable performance. Specifically, the NAEOM 104:

(i) direct energy harvested from the regenerative braking module 101 to provide instant acceleration, charge the battery 106, or convert excess energy into hydrogen storage;
(ii) allocate solar energy preferentially for powering the drive train or battery 106 charging, depending on predicted sunlight intensity and availability; and
(iii) draw additional power from the hydrogen fuel cell generator 102 when high load conditions or low renewable energy availability require stabilization of vehicle performance. By continuously learning from historical and real-time data, the decision layer refines energy distribution strategies over time, enabling adaptive and predictive management of multi-source energy for the hybrid electric vehicle.

[0032] In an embodiment of the present invention, a battery 106 management subsystem of the hybrid electric vehicle (HEV) is configured to receive regulated electrical power flows from the regenerative braking module 101, solar panels 103, and hydrogen fuel cell generator 102. The subsystem comprises a battery 106 pack, battery 106 management system (BMS) sensors, voltage and current regulators, and a control unit. BMS sensors continuously monitor battery 106 voltage, current, temperature, and state of charge (SoC), transmitting this information to the control unit for real-time evaluation of battery 106 health and charging requirements. The control unit ensures that incoming energy is applied according to optimal charging cycles, preventing overcharging, deep discharge, and thermal stress, thereby extending battery 106 life. When energy generation exceeds immediate vehicle demand, excess power from solar panels 103 or the regenerative braking module 101 is either stored in the battery 106 for later use or, if storage limits are reached, redirected for hydrogen conversion via the fuel cell generator 102. This dual-path energy handling enables efficient utilization of all harvested energy, maximizes vehicle energy autonomy, and maintains balanced power supply under varying driving and environmental conditions.

[0033] Over time, the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 refines its decision-making by analyzing historical driving data, energy consumption patterns, and vehicle performance metrics. Using neuromorphic computing and spiking neural networks (SNNs), the NAEOM 104 identifies correlations between driving behavior, terrain, load conditions, and energy utilization, enabling it to predict optimal energy allocation strategies for future driving scenarios. This continuous learning and adaptive refinement allow the NAEOM 104 to dynamically adjust the distribution of power from regenerative braking, solar panels 103, and the hydrogen fuel cell generator 102, thereby improving overall energy efficiency, reducing thermal and electrical stress on the battery 106, and maximizing energy recovery. By proactively anticipating energy demands and vehicle operating conditions, the NAEOM 104 ensures that the hybrid electric vehicle (HEV) operates with enhanced efficiency, prolonged battery 106 life, and sustained performance under varying environmental and driving conditions.

[0034] Over time, the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 refines its decision-making by analyzing historical driving data, energy consumption patterns, and vehicle performance metrics. Using neuromorphic computing and spiking neural networks (SNNs), the NAEOM 104 identifies correlations between driving behavior, terrain, load conditions, and energy utilization, enabling it to predict optimal energy allocation strategies for future driving scenarios. This continuous learning and adaptive refinement allow the NAEOM 104 to dynamically adjust the distribution of power from regenerative braking, solar panels 103, and the hydrogen fuel cell generator 102, thereby improving overall energy efficiency, reducing thermal and electrical stress on the battery 106, and maximizing energy recovery. By proactively anticipating energy demands and vehicle operating conditions, the NAEOM 104 ensures that the hybrid electric vehicle (HEV) operates with enhanced efficiency, prolonged battery 106 life, and sustained performance under varying environmental and driving conditions.

[0035] The hybrid electric vehicle (HEV) includes the cloud-based energy monitoring module 107 configured to collect, upload, and analyze operational data from onboard systems, including the Neuro-Adaptive Energy Optimization Module (NAEOM) 104, battery 106 management subsystem, and individual energy sources such as regenerative braking, solar panels 103, and the hydrogen fuel cell generator 102. The module establishes a secure communication link with remote servers to retrieve predictive insights, including weather forecasts, traffic conditions, solar availability, and efficiency optimization recommendations.

[0036] The hybrid electric vehicle (HEV) includes a user interface communicatively coupled to the cloud-based energy monitoring module 107 via a communication module, which is linked to the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 to establish a wireless connection with a computing unit, including but not limited to a smartphone, tablet, or laptop. The communication module comprises, without limitation, a Wi-Fi module, Bluetooth module, or GSM module, with Wi-Fi being the preferred embodiment. The Wi-Fi module operates as a hardware component that enables wireless data exchange between the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 and the computing unit by utilizing radio waves in accordance with IEEE 802.11 standards for wireless local area networking (WLAN). Through this connection, the user interface displays real-time energy distribution among all power sources, battery 106 health status, vehicle efficiency metrics, and predictive forecasts of future energy usage. By providing continuous feedback and actionable insights, the cloud-based module facilitates proactive energy management, supports driver decision-making, and allows remote monitoring of vehicle performance. Integration with the Neuro-Adaptive Energy Optimization Module (NAEOM) 104 further enables predictive adjustments to energy allocation based on historical energy usage data and cloud-derived forecasts, ensuring efficient utilization of all energy sources and sustained HEV performance under varying driving and environmental conditions.

[0037] The hybrid electric vehicle (HEV) operates under a continuous energy management cycle. The Neuro-Adaptive Energy Optimization Module (NAEOM) 104 perpetually senses vehicle operational parameters, including speed, load, terrain, battery 106 state of charge, and environmental conditions, through integrated sensors 105. Simultaneously, predictive inputs such as sunlight availability, weather conditions, and traffic forecasts are retrieved from the cloud-based energy monitoring module 107. Based on this combined real-time and predictive data, the NAEOM 104 dynamically distributes and optimizes energy flow among the regenerative braking module 101, solar panels 103, hydrogen fuel cell generator 102, and battery 106, ensuring efficient power utilization, enhanced battery 106 health, and sustained vehicle performance. This sensing, predicting, distributing, and optimizing cycle operates continuously and in real time, with all energy sources and subsystems functioning in an integrated manner. The cycle persists uninterrupted, adapting to changing driving conditions and energy demands, until the HEV is powered down, at which point the system ceases active management and enters a standby or shutdown state.

[0038] The present invention work best in the following manner, where the hybrid electric vehicles (HEVs) operates by integrating the regenerative braking module 101, hydrogen fuel cell generator 102, plurality of solar panels 103, Neuro-Adaptive Energy Optimization Module (NAEOM) 104, and cloud-based energy monitoring module 107 into the cohesive energy management architecture. Upon activation, the NAEOM 104 continuously receives real-time operational data from integrated sensors 105 monitoring vehicle speed, load, terrain, and driving patterns, and dynamically allocates power from regenerative braking, hydrogen fuel cells, and solar panels 103 to optimize propulsion and battery 106 charging. Energy harvested from the regenerative braking module 101 is redirected by the NAEOM 104 for instant acceleration, battery 106 charging, or conversion into hydrogen storage to maximize energy recovery and reduce battery 106 stress. Solar energy is optimized by predictive artificial intelligence (AI) protocols that estimate sunlight availability based on environmental conditions, weather forecasts, and vehicle demand, enabling real-time adjustment of battery 106 charging and power distribution. The hydrogen fuel cell generator 102 provides stable baseline power under high load or low renewable energy conditions, complementing energy from regenerative braking and solar panels 103. The NAEOM 104 employs neuromorphic computing and spiking neural networks (SNNs) to learn from historical energy usage and adaptively refine energy allocation over time, improving vehicle efficiency and extending battery 106 life. The cloud-based energy monitoring module 107 collects operational data, tracks energy distribution, battery 106 health, and vehicle efficiency, and communicates this information through the user interface installed on computing unit, enabling predictive insights, remote monitoring, and proactive energy management. This integrated system ensures continuous, optimized, and adaptive energy utilization, maintaining sustained HEV performance under varying driving and environmental conditions.

[0039] 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:1) A multi-source energy management system for hybrid electric vehicles (HEVs), the system comprising:

a) A regenerative braking module 101 constructed to harvest energy during braking of the HEV;
b) A hydrogen fuel cell generator 102;
c) A plurality of solar panels 103 integrated into the HEV;
d) a Neuro-adaptive energy optimization module (NAEOM) 104;
e) a cloud-based energy monitoring module 107 for remote tracking and predictive energy analysis;
wherein the energy generated by the regenerative braking module 101, the hydrogen fuel cell generator 102 and the plurality of solar panels 103 is regulated by the NAEOM 104 in real-time to power the HEV and also charge a battery 106 of the HEV.

2) The multi-source energy management system for HEVs as claimed in claim 1, wherein the NAEOM 104 is configured to distribute power from regenerative braking, hydrogen fuel cells, and solar panels 103 based on real-time driving conditions in HEVs.

3) The multi-source energy management system for HEVs as claimed in claim 1, wherein the NAEOM 104 is configured to continuously monitor vehicle load, terrain, and driving patterns through integrated sensors 105 to determine an efficient energy source out of the multiple energy sources for driving the HEV at any moment by employing spiking neural networks (SNNs) to improve energy flow.

4) The multi-source energy management system for HEVs as claimed in claim 1, wherein the NAEOM 104 is configured to redirect energy generated from regenerative breaking module for instant acceleration, battery 106 charging, or conversion into hydrogen storage for maximizing energy recovery and preventing stress on the battery 106.

5) The multi-source energy management system for HEVs as claimed in claim 1, wherein the NAEOM 104 is configured to optimize solar energy into powering HEV by predicting solar availability using predictive artificial intelligence (AI) protocols, based on the predictions from weather prediction database, charging of the battery 106 from the solar panels 103 is regulated depending upon on sunlight intensity, vehicle’s drive train powering demand, and weather predictions in real time.

6) The multi-source energy management system for HEVs as claimed in claim 1, wherein the NAEOM 104 employs neuromorphic computing to learn and predict optimal energy distribution requirements of the HEV over time by analyzing historical energy usage of the HEV, the NAEOM 104 continuously refines power allocation, improving vehicle efficiency and increasing battery 106 life.

7) The multi-source energy management system for HEVs as claimed in claim 1, wherein the cloud-based energy monitoring module 107 is configured to display energy distribution, battery 106 health, and vehicle efficiency metrics through a communicatively coupled user interface installed on a computing unit.