Battery thermal charging control

Abstract

A method for managing a charging process of a battery pack for an electric vehicle is provided herein. The method includes initiating a charging cycle for the battery pack, actively managing temperature of the battery pack during the charging cycle by flowing a temperature-controlled coolant through a thermal management system integrated with the battery pack, monitoring state-of-charge (SOC) and temperature of the battery pack, adjusting a charging current and a coolant temperature of the temperature-controlled coolant based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation, and terminating the charging cycle based on the target SOC and the target temperature range being achieved within predefined constraints.

Description

BATTERY THERMAL CHARGING CONTROL CROSS-REFERENCE TO RELATED APPLICATION

[0001] This patent application claims the benefit of U.S. Provisional Patent Application No. 63 / 668,178, filed July 6, 2024, which is incorporated by reference herein in its entirety. TECHNICAL FIELD

[0002] The present disclosure relates to battery management systems for electric vehicles, and more particularly to optimizing charging and thermal management of battery packs for electric vertical takeoff and landing (eVTOL) aircraft. BACKGROUND

[0003] Electric vertical takeoff and landing (eVTOL) aircraft represent a class of aerial vehicles that leverage electric power to ascend, descend, and cruise in the air. The operation of these aircraft relies heavily on the performance of their battery packs, which are integral for providing the necessary power for flight. The charging process of these battery packs is a complex procedure that must balance several factors, including the time required to charge the batteries, the longevity of the battery cells, and the safety of their operation.

[0004] The state-of-charge (SOC) and temperature of the battery packs are parameters that need to be managed during the charging process to prepare the aircraft for its next mission. The SOC indicates the remaining capacity of the battery, while the temperature can affect the battery's performance and lifespan. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0006] FIG. 1 is a schematic diagram illustrating the components of ground support equipment designed to provide charging and other support services to electric-powered aircraft, according to some examples.

[0007] FIG. 2 is a schematic diagram showing a detailed view of a charging system for an electric vertical takeoff and landing (eVTOL) aircraft, including the charging station and battery packs, according to some examples.

[0008] FIG. 3 is a block diagram illustrating a charging system for an aircraft, according to some examples.

[0009] FIG. 4 is a schematic diagram showing a detailed view of a coupled electro-thermal model used for simulating the behavior of an eVTOL aircraft battery pack, according to some examples.

[0010] FIG. 5 is a schematic diagram illustrating an electric vehicle with multiple battery packs, according to some examples.

[0011] FIG. 6 is a schematic diagram showing a detailed view of a battery pack system architecture, including thermal management components and control units, according to some examples.

[0012] FIG. 7 is a flowchart illustrating a method for managing a charging process of a battery pack, according to some examples.

[0013] FIG. 8 is a flowchart illustrating a method for charging a battery pack of an electric vehicle, detailing steps to achieve desired state-of-charge and temperature while adhering to operational constraints, according to some examples.

[0014] FIG. 9 is a flowchart illustrating the optimal control algorithm for managing the charging process of an eVTOL aircraft battery pack, focusing on the trade-off between charging time and thermal management, according to some examples.

[0015] FIG. 10 is a graphical diagram illustrating the time history of normalized charging current, cell voltage, and anode plating overpotential at 40°C during a charging cycle, according to some examples.

[0016] FIG. 11 is a graphical diagram illustrating a normalized plating-free charging current lookup table with starting state of charge and minimum plating overpotential values, according to some examples.

[0017] FIG. 12 is a schematic diagram illustrating the arrangement of a battery cell and its associated cooling system in an electric vehicle battery pack, and a corresponding ECM, according to some examples.

[0018] FIG. 13 is a graphical diagram illustrating the optimal solution for charging from a state-of-charge of 30% to 80% and cooling from a normalized temperature of 0.90 to 0.45, according to some examples.

[0019] FIG. 14 is a graphical diagram illustrating the optimal solution for charging from a state-of-charge of 30% to 60% and cooling from a normalized temperature of 0.90 to 0.65, according to some examples.

[0020] FIG. 15 is a graphical diagram illustrating the optimal solution for charging from a state-of-charge of 65% to 95% and cooling from a normalized temperature of 0.82 to 0.45, according to some examples.

[0021] FIG. 16 is a plan view of a VTOL aircraft that may comprise an electric vehicle, according to some examples.

[0022] FIG. 17 is a schematic view of an aircraft energy storage system that may be managed by the energy management system (EMS), according to some examples.

[0023] FIG. 18 illustrates an electrical architecture for the aircraft, showing the integration of multiple systems and components, according to some examples.

[0024] FIG. 19 is a diagrammatic representation of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to some examples. DETAILED DESCRIPTION

[0025] The following description sets forth exemplary aspects of the present disclosure. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure. Rather, the description also encompasses combinations and modifications to those exemplary aspects described herein. OVERVIEW

[0026] Electric vehicles, including vertical takeoff and landing (eVTOL) aircraft, rely on battery technology for power. The charging process for these battery packs involves complex considerations to balance various factors. The state-of-charge (SOC) and temperature of the battery packs are parameters that may require management during charging to prepare the vehicle for its next operation.

[0027] Rapid charging of battery packs can be beneficial for reducing turnaround times and maximizing vehicle utility. However, fast charging may potentially lead to increased degradation of battery cells, which can affect their service life and safety. Degradation mechanisms, such as lithium plating, may occur during charging, particularly if the battery cells are not within their ideal temperature range.

[0028] The thermal management of battery packs during charging presents challenges due to the heat generated during the charging process. Maintaining the battery pack within an appropriate temperature range can influence both the charging efficiency and the long-term performance of the batteries.

[0029] The described examples relate to methods and systems for optimizing the charging process of battery packs in electric vehicles, such as eVTOL aircraft. These examples may incorporate a coupled electro-thermal model that integrates the behavior of the battery, charger, and cooling system to formulate an optimal control problem. This problem can be solved using a direct collocation approach to determine charging current and thermal management profiles. The solution may aim to achieve a desired or predetermined state-of- charge (SOC) and temperature within a reduced timeframe while seeking to ensure the battery's longevity and safety.

[0030] In some examples, the system may include ground support equipment (GSE) for charging electric aircraft battery packs. The GSE may comprise a charger that receives power from a power supply network through AC supply hardware. The charger may contain power modules and a control box for managing the charging operations. An energy storage system may be included in the GSE, connected to the power supply network via the AC supply hardware. This energy storage system can provide backup power or help manage peak loads during charging operations.

[0031] A thermal conditioning system may be incorporated into the GSE to manage the temperature of battery packs during charging. The thermal conditioning system may comprise a chiller, a coolant reservoir, and a pump. The chiller can cool the coolant stored in the coolant reservoir, while the pump may circulate the coolant through the system. This arrangement may allow for precise temperature control of the battery packs during the charging process, which can be critical for optimizing charging efficiency and preserving battery life.

[0032] Cable bundles may route power, coolant, and data connections between components. Connectors may interface with charge ports on the aircraft to enable transfer of power and coolant. Dispensers can provide structural support for the cable bundles and connectors, facilitating ease of use and maintenance.

[0033] A charging station controller may manage the charging and thermal conditioning processes. The charging station controller may receive data from and control the operation of the dispensers and associated components. In some cases, the charging station controller may store and execute battery models and charge control algorithms. These models and algorithms may enable the charging station controller to control the charging process based on various factors such as battery state, temperature, and charging history.

[0034] The system may also include a data offload server that collects operational data from the charging processes. The data offload server may connect through a network to amonitoring and control center with an associated datastore. The monitoring and control center can provide oversight and control of the charging operations. This data collection and management system may allow for continuous improvement of charging strategies, predictive maintenance, and performance analysis of the battery packs and charging infrastructure.

[0035] In operation, when an aircraft connects to the GSE via the charge port, the charging station controller may initiate a charging cycle. The charging station controller may communicate with the aircraft's battery management system to determine the current state of charge and temperature of the battery pack. Based on this information and the stored battery models, the charging station controller can determine an optimal charging profile.

[0036] The charging station controller may then control the power modules in the charger to deliver the appropriate charging current to the battery pack. Simultaneously, the charging station controller may regulate the thermal conditioning system to maintain the battery pack within the optimal temperature range for charging. This may involve adjusting the coolant temperature via the chiller and controlling the coolant flow rate using the pump.

[0037] Throughout the charging process, the charging station controller may continuously monitor the battery pack's state of charge, temperature, and other relevant parameters. The charging station controller can adjust the charging current and coolant temperature in real- time based on this feedback to control the charging process.

[0038] The data offload server may record all relevant data from the charging process, including charging current profiles, temperature profiles, and any anomalies or events. This data can be transmitted to the monitoring and control center for analysis and storage in the datastore. The collected data may be used for various purposes, such as optimizing charging algorithms, predicting battery degradation, and scheduling maintenance for the GSE.

[0039] In some examples, the electric vehicle may include multiple battery packs, with each battery pack containing battery modules. Each battery module may include multiple battery cells. The battery cells may be large format pouch cells with a nickel manganese cobalt (NMC) cathode and a graphite anode. The battery packs may incorporate cold plates between the battery cells for thermal management. These cold plates can be part of a fluid circulation system that transfers heat away from the battery cells to maintain optimal operating temperatures during both charging and discharging operations.

[0040] The electric vehicle may include an energy management system (EMS) that communicates with the battery packs. The EMS may comprise an EMS computer and memory. The memory may store battery models that are accessed by the EMS computer toperform various energy management functions. These functions can include power allocation, thermal management, state estimation, and charge / discharge control across multiple battery packs.

[0041] Each battery module may include a battery management system (BMS) that monitors and controls the operation of the battery cells. The BMS may communicate with the EMS to provide battery status information and receive control commands. The BMS can include temperature sensors, voltage sensors, and current sensors to monitor the state of individual cells within each module.

[0042] The battery models stored in memory may include look-up tables that contain parameters used by the EMS to manage the battery packs. These look-up tables can help capture nonlinear effects of battery state of charge, temperature, age, and discharge current on model dynamics. For example, the look-up tables may correlate cell temperature and terminal voltage with permissible charging currents to prevent degradation mechanisms such as lithium plating.

[0043] In some cases, the EMS may use the battery models to estimate the state of charge and state of health of the battery packs. The EMS may also use the battery models to predict the available power and energy capacity of the battery packs under different operating conditions. These models can include equivalent circuit models to represent electrical characteristics and multi-node thermal models to simulate temperature distribution within the battery cells and cooling system interactions.

[0044] The BMS may monitor various parameters of the battery cells, such as voltage, current, and temperature. The BMS may use this information to ensure safe operation of the battery cells and to balance the state of charge across all cells in a battery module. The BMS can implement cell balancing algorithms to equalize the state of charge between cells, which helps extend battery life and maintain pack performance.

[0045] In some cases, the EMS may communicate with the GSE during charging operations. The EMS may provide information about the battery packs to the charging station controller, which can use this information along with the electro-thermal model to control the charging process. This bidirectional communication enables adaptive charging strategies that can be tailored to the specific condition and requirements of the battery packs.

[0046] The components of the electric vehicle battery system may be arranged in a hierarchical structure, with the EMS at the system level communicating with multiple BMSs at the module level, which in turn monitor individual battery cells. This architecture mayenable coordinated management of the electric vehicle's energy storage system. The hierarchical approach allows for distributed processing of battery data, with local BMSs handling cell-level monitoring and protection while the EMS coordinates system-level functions and interfaces with other vehicle systems and external equipment.

[0047] These described examples may effectively balance the need for rapid charging with the requirements for maintaining battery temperature within specific operational thresholds to prevent premature degradation. By integrating advanced modeling techniques with real- time monitoring and control systems, these examples seek to enhance the efficiency, safety, and longevity of battery packs in electric vehicles, particularly in demanding applications such as eVTOL aircraft. FIG. 1: GROUND SUPPORT EQUIPMENT (GSE) 102

[0048] FIG. 1 is a system diagram showing a detailed view of ground support equipment (GSE) 102 configured to provide charging and thermal management services to battery packs 104 of electric-powered aircraft, according to some examples.

[0049] The ground support equipment 102 interfaces with a power supply network 106. The power supply network 106 may be a utility grid or a dedicated local generation source. Electrical power from the power supply network 106 is routed through AC supply hardware 108. The AC supply hardware 108 may include transformers, rectifiers, and protection circuitry to condition and convert incoming AC power for downstream use.

[0050] A charger 110 receives conditioned power from the AC supply hardware 108. The charger 110 comprises multiple power modules 112 and a control box 114. The power modules 112 may be configured to independently charge multiple battery packs 104, allowing for isolated and redundant charging operations. Each power module 112 may include power conversion circuitry, such as DC-DC converters, and may be capable of adjusting output voltage and current based on the requirements of the connected battery pack 104. The control box 114 coordinates the operation of the power modules 112, managing current, voltage, and temperature parameters during charging. The control box 114 may include microcontrollers, safety relays, and communication interfaces for real-time monitoring and control. In some examples, the control box 114 may implement safety interlocks and fault detection logic to isolate or shut down individual power modules 112 in response to detected anomalies.

[0051] The ground support equipment 102 further includes an energy storage system 116. The energy storage system 116 may be a bank of batteries or other energy storage devices, such as supercapacitors. The energy storage system 116 is connected to the power supplynetwork 106 via the AC supply hardware 108 and may store electrical energy during periods of low demand or low cost. The stored energy may be used to supplement or replace grid power during peak demand or grid outages. In some examples, the energy storage system 116 may be configured to provide power smoothing or load leveling functions.

[0052] A thermal conditioning system 118 is integrated within the ground support equipment 102. The thermal conditioning system 118 comprises a chiller 120, a coolant reservoir 122, and a pump 124. The chiller 120 may use a vapor-compression or thermoelectric cooling cycle to reduce the temperature of a coolant fluid. The coolant reservoir 122 stores the chilled coolant, which is circulated by the pump 124. The pump 124 may be a variable-speed or fixed-speed pump, and may be selected based on the required flow rate and pressure for the application. The thermal conditioning system 118 is configured to maintain the temperature of the battery packs 104 within a specified range during charging operations.

[0053] Coolant is delivered from the thermal conditioning system 118 to the battery packs 104 via a cable bundle 126, dispenser 132 and cable bbundle144.. The cable bundles 126 and 144 may include hoses for coolant, electrical conductors for power delivery, and data lines for communication. The cable bundles 144 terminate at connectors 128, which interface with charge ports 130 on the battery packs 104. The connectors 128 may include quick-disconnect fittings, thermal sensors, and electrical contacts. In some examples, the connectors 128 may be equipped with charge handles for manual or automated connection to the aircraft.

[0054] The dispenser 132 is coupled to the cable bundles 126 and 144 and may provide structural support, routing, and storage for the connectors 128. The dispenser 132 may include docking stations for stowing connectors 128 when not in use. The dispenser 132 is managed by a charging station controller 134. The charging station controller 134 regulates the delivery of electrical power and coolant to the battery packs 104. The charging station controller 134 may include embedded processors, memory, and software for executing charge control algorithms. The charging station controller 134 may receive feedback from sensors located throughout the ground support equipment 102 and the battery packs 104, including temperature, voltage, and current sensors.

[0055] The charging station controller 134 is connected to a data offload server 136. The data offload server 136 collects and stores operational data, such as charging session logs, telemetry from the battery packs 104, and status information from the thermal conditioningsystem 118. The data offload server 136 may be configured to transfer data to external systems for analysis, maintenance scheduling, or regulatory compliance.

[0056] A monitoring and control center 138 is linked to the ground support equipment 102 via a network 140. The monitoring and control center 138 oversees the operation of multiple charging stations, manages scheduling, and may issue commands to adjust charging parameters or respond to detected faults. The monitoring and control center 138 is associated with a datastore 142, which archives operational data for long-term analysis and reporting.

[0057] The network 140 may be a wired or wireless communication network, supporting data exchange between the charging station controller 134, data offload server 136, monitoring and control center 138, and datastore 142. The network 140 may also facilitate firmware updates and remote diagnostics.

[0058] In some examples, the system may be configured to support multiple aircraft simultaneously, with each battery pack 104 receiving charging and thermal management tailored to the state of charge, temperature, and operational history of the specific battery pack. The modular architecture of the power modules 112 and the use of independent cable bundles 126 and connectors 128 enable flexible adaptation to various aircraft configurations and battery technologies.

[0059] Alternative configurations may include additional or alternative thermal management components, such as heaters for pre-conditioning battery packs 104 in cold environments, or redundant pumps 124 for increased reliability. The energy storage system 116 may be omitted in installations where grid reliability is sufficient, or may be replaced with alternative storage technologies.

[0060] Overall, FIG. 1 illustrates a system architecture in which ground support equipment 102 integrates electrical, thermal, and data management subsystems to support the charging and maintenance of battery packs 104 for electric-powered aircraft. FIG. 2: EVTOL AIRCRAFT BATTERY CHARGING SYSTEM

[0061] FIG. 2 is a system diagram showing a detailed view of a charging system 200 for an electric vertical takeoff and landing (eVTOL) aircraft, according to some examples. FIG.2 presents a view focused on the interaction between a charging station 202, a battery pack 204, and a thermal management system 206, while emphasizing these components relative to the broader ground support equipment 102 architecture shown in FIG. 1.

[0062] The charging system 200 includes a charging station 202. The charging station 202 comprises a control unit 208 and a power supply unit 210. The control unit 208 may regulate charging current and voltage delivered to the battery pack 204. The power supply unit 210 may convert incoming electrical energy to a form suitable for charging the battery pack 204. In some examples, the control unit 208 may receive sensor data from the battery pack 204 and adjust charging parameters in real time.

[0063] The battery pack 204 includes multiple cells 212. In some examples, the cells 212 are large format pouch cells. The battery pack 204 is equipped with sensors 214. The sensors 214 may include temperature sensors, voltage sensors, and current sensors. These sensors 214 may provide real-time measurements of cell temperature, state-of-charge, and voltage to the control unit 208. The battery pack 204 may be configured to interface with external thermal management and charging systems via electrical and fluidic connections.

[0064] The thermal management system 206 is configured to maintain the temperature of the battery pack 204 within a specified range during charging. The thermal management system 206 includes a thermal management unit 216, cooling components 218, heating components 220, pumps 222, heat exchangers 224, and coolant flow channels 226. The thermal management unit 216 may coordinate the operation of the cooling components 218 and heating components 220. The cooling components 218 may include liquid cooling systems, fans, or other heat rejection devices. The heating components 220 may include electric heaters or other thermal sources.

[0065] The pumps 222 may circulate coolant through the coolant flow channels 226. The coolant flow channels 226 may be arranged to provide thermal contact with the cells 212. The heat exchangers 224 may transfer heat between the coolant and the external environment or other subsystems. In some examples, the thermal management unit 216 may receive input from the sensors 214 and adjust the operation of the cooling components 218, heating components 220, and pumps 222 to maintain the battery pack 204 within a target temperature range.

[0066] The configuration shown in FIG.2 allows for the integration of charging and thermal management operations. The control unit 208 may coordinate with the thermal management unit 216 to adjust charging current and thermal conditioning in response to real-time sensor data. The system may support charging strategies that account for battery temperature, state-of-charge, and operational constraints.

[0067] In some examples, the thermal management system 206 may be implemented with alternative components, such as redundant pumps 222, additional heat exchangers 224, orvariable-speed fans. The coolant flow channels 226 may be configured for parallel or series flow, depending on the thermal requirements of the battery pack 204. The charging station 202 may be implemented as a standalone unit or as part of a larger ground support equipment 102 installation.

[0068] The system shown in FIG. 2 may be adapted for use with different battery chemistries, cell formats, or aircraft configurations. The modular arrangement of the charging station 202, battery pack 204, and thermal management system 206 may support flexible integration with various electric vehicle platforms. FIG. 3:MODEL-BASED CHARGING CONTROL

[0069] FIG. 3 is a system diagram showing a functional view of a data-driven battery management and charging control system 300 for an aircraft 302, according to some examples.

[0070] The aircraft 302 includes a battery pack 104 and a battery management system 304. The battery management system 304 is configured to collect and process operational data 306. The operational data 306 may include vehicle sensor data 308 and limit data 310. The vehicle sensor data 308 may comprise real-time measurements from sensors distributed throughout the aircraft 302, such as temperature sensors, voltage sensors, current sensors, and state-of-charge (SOC) estimators. The limit data 310 may define operational boundaries for the battery pack 104, such as maximum allowable charging current, voltage thresholds, and temperature limits. The operational data 306 may further include derived or estimated parameters, such as predicted battery health metrics, thermal gradients, and fault detection indicators.

[0071] The battery management system 304 may aggregate the vehicle sensor data 308 and limit data 310 to generate the operational data 306. The operational data 306 is transmitted to the ground support equipment 102 via an aircraft data interface 312. The aircraft data interface 312 may implement a communication protocol for secure and reliable data transfer. The operational data 306 is sent over a communication channel 314 to a ground data interface 316 of the ground support equipment 102.

[0072] The ground support equipment (GSE) 102 includes a thermal conditioning system 118, which may regulate the temperature of the battery pack 104 during charging. The thermal conditioning system 118 may include chillers, pumps, and coolant reservoirs, and may be configured to deliver temperature-controlled coolant to the battery pack 104. The ground support equipment (GSE) 102 also includes a charging station controller 134. The charging station controller 134 may receive operational data 306 from the ground datainterface 316 and GSE sensor data 318. The GSE sensor data 318 may include measurements from sensors within the ground support equipment (GSE) 102, such as coolant temperature, flow rate, and electrical power sensors.

[0073] The charging station controller 134 may execute charge control algorithms 320. The charge control algorithms 320 may use operational data 306, GSE sensor data 318, and battery models 322 to determine charging parameters. The battery models 322 may include an electro-thermal model 324. The electro-thermal model 324 may integrate an equivalent circuit model (ECM) 326 and a multi-node thermal model 328. The equivalent circuit model 326 may represent the electrical behavior of the battery pack 104 using circuit elements such as resistors and capacitors, with parameters that may be functions of SOC, temperature, and current. The multi-node thermal model 328 may represent the temperature distribution within the battery pack 104, using multiple nodes to capture thermal gradients and heat transfer dynamics.

[0074] The charging station controller 134 accesses look-up tables 330. The look-up tables 330 may store precomputed data such as plating-free current limits, which may be indexed by battery terminal voltage and temperature. The charge control algorithms 320 reference the look-up tables 330 to determine safe charging currents under varying conditions. In some examples, the look-up tables 330 may be generated using off-line electrochemical simulations or experimental measurements.

[0075] The charging station controller 134 may use the outputs of the electro-thermal model 324 to dynamically adjust charging current and thermal management setpoints. The charge control algorithms 320 implement optimization routines to minimize charging time while maintaining the battery pack 104 within operational constraints defined by the limit data 310. The system may support real-time adaptation to changes in battery state, environmental conditions, or operational requirements.

[0076] In some examples, the system is configured to support multiple aircraft 302, with each battery pack 104 receiving individualized charging and thermal management based on operational data 306 specific to the battery pack. The architecture may allow for integration of additional models or algorithms, such as degradation models or predictive maintenance routines, within the framework of the battery models 322.

[0077] The communication channel 314 may support wired or wireless protocols, and may be configured for secure data transmission. The ground data interface 316 and aircraft data interface 312 may implement error checking and data validation routines to ensure data integrity.

[0078] Overall, FIG. 3 illustrates a system in which operational data from an aircraft 302 is used by ground support equipment 102 to implement model-based charging and thermal management strategies, leveraging advanced battery models 322, charge control algorithms 320, and look-up tables 330 to optimize battery performance and safety. FIG. 4: COUPLED ELECTRO-THERMAL MODEL SCHEMATIC

[0079] FIG. 4 shows a schematic diagram of a coupled electro-thermal model 400 for simulating the behavior of an eVTOL aircraft battery pack, according to some examples.

[0080] The coupled electro-thermal model 400 comprises a multi-node thermal model 402 and an equivalent circuit model 404. The multi-node thermal model 402 is configured to represent the spatial and temporal evolution of temperature within the battery pack. The multi-node thermal model 402 includes a plurality of thermal nodes 406, each corresponding to a specific region or layer within the battery, such as the central region, surface, or interface with the cooling system. Each thermal node 406 is associated with a heat capacity 408, which quantifies the amount of thermal energy required to change the temperature of that region. Thermal resistances 410 are disposed between adjacent thermal nodes 406 and model the conductive heat transfer between these regions. The arrangement of thermal nodes 406, thermal resistances 410, and heat capacities 408 enables the model to capture temperature gradients and transient thermal behavior within the battery during operation.

[0081] The multi-node thermal model 402 is thermally coupled to a cooling system 412. The cooling system 412 is configured to remove heat from the battery pack, typically by circulating a coolant in thermal contact with one or more of the thermal nodes 406. The cooling system 412 may be modeled as a boundary node with its own heat capacity and thermal resistance to the battery, or as a dynamic system with its own temperature evolution. The interaction between the battery and the cooling system 412 enables the model to simulate the effects of active thermal management strategies, such as varying coolant temperature or flow rate, on the internal temperature distribution of the battery.

[0082] The equivalent circuit model 404 is configured to represent the electrical behavior of the battery pack. The equivalent circuit model 404 includes an open circuit voltage OCV 414, a plurality of internal resistances 416, and a dynamic response 418. The open circuit voltage OCV 414 is a function of the battery’s state-of-charge (SOC) and temperature, and may be parameterized by a look-up table or empirical function. The internal resistances 416 include an ohmic resistance and one or more resistor-capacitor (RC) pairs, each with a resistance and a time constant. The dynamic response 418 is modeled by the voltages acrossthe RC pairs, which capture the transient electrical response of the battery to changes in current.

[0083] The mathematical formulation of the equivalent circuit model 404 typically includes the following equations:

[0084] The SOC dynamics: ^^^^^^^^ ^^^^ ^^^^^= ^ app^^^^^^^^^^^^^^ where ^^^^ is the state-of-charge, ^^^^appis the applied current, ^^^^ is the nominal capacity, and ^^^^^^^^is the coulombic efficiency.

[0085] The RC pair voltages:where ^^^^^^^^is the voltage across the ^^^^i-th RC pair, ^^^^^^^^is the resistance, and ^^^^^^^^τi is the time constant.

[0086] The terminal voltage:where ^^^^OCVis the open circuit voltage, ^^^^ is the temperature, and is the series resistance.

[0087] The multi-node thermal model 402 and the equivalent circuit model 404 are coupled through several mechanisms. The heat generation in the battery, which serves as an input to the thermal model, is computed from the electrical model as: ^^^^where the first term represents irreversible (Joule) heating and the second term represents reversible (entropic) heating. The heat generation is distributed among the thermal nodes 406, typically with the majority assigned to the central nodes.

[0088] Conversely, the electrical parameters in the equivalent circuit model 404, such as the internal resistances 416 and time constants, are functions of temperature and SOC. These dependencies are captured by parameterizing the resistances and time constants usinglook-up tables or empirical relationships, with temperature values provided by the thermal nodes 406. This bidirectional coupling allows the model to capture the impact of temperature on electrical performance and the effect of electrical operation on thermal behavior.

[0089] The cooling system 412 interacts with the outermost thermal node 406, providing a heat sink that can be dynamically controlled. The cooling system 412 may be modeled with its own heat capacity and thermal resistance, and the temperature of the cooling system 412 may be set by an external chiller or thermal management unit. The capability to simulate the effect of the cooling system 412 allows the model to assess various thermal management strategies and their influence on battery safety and performance.

[0090] The dynamic response 418 of the equivalent circuit model 404 captures the time- dependent voltage behavior of the battery under varying current loads, including both fast and slow transients. This plays a significant role in accurately predicting battery voltage during rapid charging or discharging events, which in turn affects the calculation of heat generation and the assessment of safety constraints.

[0091] In summary, the coupled electro-thermal model 400 illustrated in FIG. 4 provides a comprehensive framework for simulating the electrical and thermal behavior of a battery pack under various operating conditions. The integration of the multi-node thermal model 402, including thermal nodes 406, thermal resistances 410, and heat capacities 408, with the equivalent circuit model 404, including open circuit voltage OCV 414, internal resistances 416, and dynamic response 418, enables detailed analysis of the interactions between electrical and thermal domains. The inclusion of the cooling system 412 further allows the model to assess the effectiveness of active thermal management strategies. This model can be used for real-time control, predictive simulation, or offline optimization of charging and thermal conditioning protocols for eVTOL aircraft battery systems. FIG. 5: ELECTRIC VEHICLE BATTERY SYSTEM ARCHITECTURE

[0092] FIG. 5 is a system diagram showing a detailed view of an electric vehicle 502 battery system, according to some examples.

[0093] The electric vehicle 502 includes a battery pack 104. The battery pack 104 comprises multiple battery modules 504. Each battery module 504 contains a plurality of battery cells 506. The battery modules 504 are configured to operate in an isolated or interconnected manner, depending on the vehicle architecture. In some examples, the battery modules 504 may be arranged to support independent monitoring and control of each module.

[0094] Each battery module 504 is coupled to a battery management system (BMS) 508. The BMS 508 is configured to monitor and regulate electrical and thermal parameters of the battery cells 506. The BMS 508 tracks state-of-charge, voltage, current, and temperature for each battery cell 506. In some examples, the BMS 508 may implement balancing algorithms to equalize the state-of-charge across the battery modules 504. The BMS 508 may also enforce safety limits by adjusting charge and discharge rates or by isolating modules in response to detected anomalies.

[0095] The battery pack 104 interfaces with an energy management system (EMS) 510. The EMS 510 comprises an EMS computer 512. The EMS computer 512 is configured to execute battery models 322 stored in a memory 514. The battery models 322 may include coupled electro-thermal models that simulate the electrical and thermal behavior of the battery pack 104 under various operating conditions. In some examples, the battery models 322 may incorporate equivalent circuit models, multi-node thermal models, or other physics-based or data-driven models.

[0096] The memory 514 stores look-up tables 516. The look-up tables 516 may provide parameterized data for battery model elements, such as resistance, time constants, open- circuit voltage, or other state-dependent variables. The look-up tables 516 may be multi- dimensional, with axes corresponding to state-of-charge, temperature, current, or age of the battery cells 506. In some examples, the look-up tables 516 may be updated based on laboratory characterization, in-field measurements, or adaptive algorithms.

[0097] The EMS 510 is configured to receive data from the BMS 508 and to use the battery models 322 and look-up tables 516 to estimate battery state and predict future behavior. The EMS 510 may implement algorithms for real-time state estimation, including state-of-charge, state-of-health, and temperature estimation. In some examples, the EMS 510 may include a thermal state estimator that uses a multi-node thermal model to estimate temperature gradients within each battery module 504 or battery cell 506. The EMS 510 may use these estimates to inform charging, discharging, and thermal management strategies.

[0098] The EMS 510 may control charging operations by adjusting current and thermal management setpoints based on model predictions and sensor feedback. In some examples, the EMS 510 may implement control algorithms such as model predictive control, direct collocation-based optimization, or rule-based logic. The EMS 510 may also coordinate with external systems, such as ground support equipment, to exchange operational data and receive charging instructions.

[0099] The architecture shown in FIG. 5 may be used in conjunction with the system shown in FIG. 3, where the electric vehicle 502 communicates with ground support equipment for charging and data exchange. FIG.5 provides a detailed view of the internal battery management and energy management systems that process operational data and implement control strategies based on real-time measurements and model predictions. In some examples, the architecture may be adapted for different vehicle configurations, battery chemistries, or operational requirements. The modular design of the battery pack 104, battery modules 504, and the integration of the BMS 508 and EMS 510 may support scalability and adaptability for various electric vehicle platforms. FIG. 6: BATTERY PACK SYSTEM ARCHITECTURE

[0100] FIG. 6 is a schematic diagram showing a detailed view of a battery pack 600 system architecture, according to some examples.

[0101] The battery pack 600 includes a plurality of battery cells 212. In some examples, the battery cells 212 are pouch cells arranged in series or parallel configurations to achieve desired voltage and capacity characteristics. The battery cells 212 are electrically coupled to a control unit 208. The control unit 208 comprises a Battery Management System (BMS) 602 and a Thermal Management Control Unit (TMCU) 604. The BMS 602 is configured to monitor and regulate electrical parameters of the battery cells 212, including state-of- charge, voltage, current, and state-of-health. The TMCU 604 is configured to manage thermal parameters of the battery pack 600, including temperature regulation and thermal protection functions.

[0102] A set of sensors 214 is coupled to the battery pack 600. The sensors 214 include a voltage sensor 606, a current sensor 608, and a temperature sensor 610. The voltage sensor 606 is configured to measure the electrical potential across the battery cells 212 or across individual cells. The current sensor 608 is configured to measure the current entering or leaving the battery pack 600. The temperature sensor 610 is configured to measure the temperature of the battery cells 212 or the temperature at specific locations within the battery pack 600. In some examples, additional sensors such as state-of-charge sensors or pressure sensors may be included.

[0103] The control unit 208 receives sensor data from the sensors 214 and processes this data to determine operational parameters for the battery pack 600. The BMS 602 may use the voltage sensor 606 and current sensor 608 data to estimate state-of-charge, detect overvoltage or undervoltage conditions, and implement cell balancing algorithms. TheTMCU 604 may use the temperature sensor 610 data to regulate thermal management operations, including activation of cooling or heating subsystems.

[0104] A thermal management unit 216 is coupled to the battery pack 600. The thermal management unit 216 includes cold plates 612 and coolant flow channels 224. The cold plates 612 are positioned in thermal contact with the battery cells 212 to facilitate heat transfer. The coolant flow channels 224 are configured to circulate a coolant fluid through the cold plates 612. In some examples, the coolant flow channels 224 are connected to an external or integrated cooling system that may include pumps, chillers, or heat exchangers. The thermal management unit 216 may operate in coordination with the TMCU 604 to maintain the battery cells 212 within a specified temperature range during charging, discharging, or standby conditions.

[0105] The control unit 208 is configured to interface with a charging station 202. The charging station 202 may provide electrical power for charging the battery pack 600 and may also provide data communication capabilities. In some examples, the control unit 208 communicates with the charging station 202 to negotiate charging parameters, report battery status, or receive charging instructions.

[0106] The architecture shown in FIG. 6 may be implemented with alternative sensor types or arrangements. For example, the sensors 214 may include distributed temperature sensors at multiple locations within the battery pack 600 to capture temperature gradients. The cold plates 612 may be constructed from materials with high thermal conductivity, such as aluminum or copper alloys. The coolant flow channels 224 may be configured for parallel or series flow, depending on the thermal management requirements.

[0107] In some examples, the BMS 602 and TMCU 604 may be implemented as separate physical modules or as integrated functions within a single microcontroller or processor. The control unit 208 may include additional safety features, such as isolation relays, fault detection logic, or redundant communication interfaces.

[0108] The data collected by the sensors 214 may be used for real-time monitoring, diagnostics, and control. The control unit 208 may log operational data for predictive maintenance or transmit data to external systems for further analysis. The integration of electrical and thermal management functions within the battery pack 600 supports coordinated operation with ground support equipment, as described in FIG.1 and FIG.3. The architecture may be adapted for use in electric vehicles, eVTOL aircraft, or stationary energy storage systems.FIG. 7: BATTERY CHARGING AND THERMAL MANAGEMENT PROCESS

[0109] FIG. 7 is a flowchart diagram showing a process view of a method 700 for managing the charging and thermal conditioning of a battery pack 104 for an electric vehicle, according to some examples.

[0110] The method 700 includes a sequence of operations that may be executed by a charging station controller 134, a battery management system, or a combination of ground support equipment 102 and vehicle-side controllers. The process may be implemented in hardware, software, or a combination thereof.

[0111] At block 702, the method 700 initiates a charging cycle for the battery pack 104. In some examples, the initiation may be triggered by a command from a battery management system or a ground support equipment 102 controller. The initiation may include verifying the current state-of-charge (SOC), temperature, and operational readiness of the battery pack 104. The process may also load predetermined constraints, such as voltage, current, and temperature limits, from a memory or look-up table 516.

[0112] At block 704, the method 700 actively manages the temperature of the battery pack 104 during the charging cycle. In some examples, a thermal conditioning system 118 (or thermal management system 206) may circulate a temperature-controlled coolant through the battery pack 104. The coolant temperature and flow rate may be regulated by pumps, valves, and a thermal management unit 216. The thermal conditioning system 118 may receive real-time feedback from temperature sensors and adjust the coolant parameters accordingly. The coolant may be supplied by a ground-based thermal conditioning system 118, which may include a chiller 120, a coolant reservoir 122, and a pump 124. The thermal conditioning system 118 may also include heating components for pre-conditioning in cold environments.

[0113] At block 706, the method 700 monitors the state-of-charge and temperature of the battery pack 104. Sensors, including voltage sensors, current sensors, and temperature sensors, may provide real-time measurements to the battery management system or charging station controller 134. The monitoring may include tracking minimum, maximum, and average cell temperatures, as well as SOC and terminal voltage. The process may use a multi-node thermal model 328 to estimate temperature gradients within the battery pack 104 or individual battery cells.

[0114] At block 708, the method 700 adjusts the charging current and the temperature of the temperature-controlled coolant in real-time. The adjustment may be based on a coupled electro-thermal model 324, which integrates an equivalent circuit model (ECM) 326 and amulti-node thermal model 328. The electro-thermal model 324 may use look-up tables 330 to parameterize circuit elements as functions of SOC, temperature, current, and age. The charging current may be limited by constraints derived from an electrochemical model, such as a plating-free current limit based on terminal voltage and minimum cell temperature. The process may implement charge control algorithms 320, such as model predictive control or direct collocation-based optimization, to determine the charging and cooling profiles. The charging station controller 134 may adjust the setpoints for current and coolant temperature to achieve the target SOC and temperature while maintaining all operational constraints.

[0115] At block 710, the method 700 terminates the charging cycle when the target SOC and the target temperature range are achieved. The termination may be based on real-time evaluation of the battery pack 104 state and confirmation that all safety and degradation constraints are satisfied. The method 700 may include a final diagnostic check and may log operational data to a data offload server 136 for further analysis or maintenance scheduling.

[0116] In some examples, the method 700 may be adapted to support multiple battery packs 104 simultaneously, with independent monitoring and control for each pack. The process may also be configured to operate with alternative thermal management architectures, such as redundant pumps or heaters, and may be implemented in various electric vehicle platforms, including eVTOL aircraft and ground vehicles. FIG. 8: FLOWCHART FOR BATTERY CHARGING STRATEGY

[0117] FIG. 8 is a flowchart diagram showing a process view of a method 800 for charging a battery pack 808 of an electric vehicle, according to some examples.

[0118] The flowchart includes a sequence of operations that may be executed by a charging station controller 134, a battery management system, or a combination of ground support equipment (GSE) 102 and vehicle-side controllers. The process may be implemented in hardware, software, or a combination thereof.

[0119] At block 802, the method 800 accesses a model that predicts the behavior of the battery pack 808 during charging. The model may be a coupled electro-thermal model 324 that integrates both electrical and thermal dynamics of the battery pack 808. In some examples, the coupled electro-thermal model 324 includes the equivalent circuit model (ECM) 326 and the multi-node thermal model 328. The equivalent circuit model (ECM) 326 may simulate electrical responses such as voltage, current, and internal resistance, while the multi-node thermal model 328 may simulate temperature gradients and heat transfer within the battery pack 808. The model may be parameterized by look-up tables 516 that capturenonlinear dependencies on state-of-charge, temperature, current, and age. The model may be stored in a memory 514 and accessed by charge control algorithms 320.

[0120] At block 804, the method 800 accesses a control problem formulated to achieve a desired state-of-charge (SOC) and temperature for the battery pack 104. The control problem may define objectives such as minimizing charging time and constraints such as voltage, temperature, and current limits. In some examples, the control problem includes constraints to prevent degradation mechanisms, such as lithium plating, by enforcing a minimum plating overpotential or by using a look-up table 516 of plating-free charging current as a function of cell temperature and terminal voltage. The control problem may also include penalties for deviation from target SOC and temperature, as well as for energy consumption of the thermal management system. The constraints may be applied at each discretization interval of the charging process.

[0121] At block 806, the method 800 seeks to solve the control problem to determine a charging strategy. The solution may be computed using a numerical optimization technique, such as direct collocation. In some examples, the direct collocation approach discretizes the time interval into segments and approximates state trajectories using polynomial interpolation, such as third-order Lagrange polynomials at Legendre-Radau points. The optimization variables may include the charging current, thermal management input, and state variables at each discretization point. The method 800 may enforce continuity constraints and operational constraints at the discretization points. The optimization may be performed using an interior-point method or other nonlinear programming solvers. The result is a charging strategy that specifies the charging current and thermal management profile required to achieve the target SOC and temperature while satisfying all constraints.

[0122] At block 808, the method 800 applies the determined charging strategy to the battery pack 104. The charging strategy may include a charging current profile and a thermal management profile. The charging current profile may be implemented by a charger 110 which adjusts the current delivered to the battery pack 104. The thermal management profile may be implemented by a thermal conditioning system 118 or a thermal management unit 216, which regulates the temperature of the battery pack 104 using coolant flow, chiller 120, pump 124, or other cooling or heating components. The process may monitor real-time data from sensors, such as temperature sensors, voltage sensors, and current sensors, to ensure that the charging process remains within operational limits. The charging strategy may be dynamically adjusted based on updated sensor data or model predictions.

[0123] In some examples, the method 800 may be adapted to support multiple battery packs simultaneously, with independent monitoring and control for each pack. The process may also be configured to operate with alternative thermal management architectures, such as redundant pumps or heaters, and may be implemented in various electric vehicle platforms, including eVTOL aircraft and ground vehicles. FIG. 9: FLOWCHART FOR BATTERY CHARGING OPTIMIZATION

[0124] FIG. 9 is a flowchart diagram showing a process view of a control algorithm 900 for managing the charging and thermal conditioning of a battery pack 104 for an electric vehicle, according to some examples.

[0125] The control algorithm 900 includes a sequence of operations that may be executed by a charging station controller 134, a battery management system, or a combination of ground support equipment 102 and vehicle-side controllers. The process may be implemented in hardware, software, or a combination thereof.

[0126] At block 902, initialization is performed. This operation may include loading computational libraries, setting up the optimization environment, and initializing variables and parameters required for the charging process. In some examples, initialization may also include establishing communication with sensors and data sources.

[0127] At block 904, the process sets initial conditions. The initial state-of-charge (SOC), temperature, and other relevant battery pack 104 parameters are established. These initial conditions may be determined by state estimation algorithms running on a battery management system or by direct sensor measurements. The initial conditions correspond to the initial values for the state variables in the coupled electro-thermal model, such as those defined in the state-space equations of the system model summary.

[0128] At block 906, predetermined constraints are loaded. These constraints may include maximum and minimum temperature limits, voltage thresholds, and plating overpotential constraints. In some examples, the constraints may be derived from manufacturer specifications, safety standards, or operational requirements. The constraints may be stored in memory or accessed from a look-up table 516. At a high level, these constraints are reflected in the optimization problem formulation, such as the bounds and inequality constraints in the optimal control problem, which limit variables like cell temperature, terminal voltage, and charging current.

[0129] At block 908, a look-up table for plating-free current limits is used. For example, the look-up table 516 may provide maximum allowable charging currents as a function of cell temperature and terminal voltage. In some examples, the look-up table 516 is generatedusing a physics-based electrochemical model or by three-electrode measurements. The use of a voltage-based look-up table may reduce sensitivity to state-of-charge estimation errors and may provide robustness against cell aging. The current limits from the look-up table are incorporated as constraints in the optimization problem, for example as a constraint that the applied charging current does not exceed the plating-free current limit as a function of minimum cell temperature and terminal voltage.

[0130] At block 910, an iterative optimization process is executed. The process may use a direct collocation approach, discretizing the time interval into segments and approximating state trajectories using polynomial interpolation, such as third-order Lagrange polynomials at Legendre-Radau points. The optimization variables may include charging current, thermal management input, and state variables at each discretization point. The process may enforce continuity constraints and operational constraints at the discretization points. In some examples, the optimization is performed using an interior-point method or other nonlinear programming solvers. The underlying system dynamics are governed by the coupled electro-thermal model equations, which include the equivalent circuit model for electrical behavior and the multi-node thermal model for thermal behavior, and the optimization seeks to minimize an objective function that may include terms for final time, slack variables, cooling energy, and current profile smoothness.

[0131] At block 912, the control algorithm 900 calculates charging and cooling rates. The charging current profile and thermal management profile are determined based on the current state, constraints, and model predictions. The thermal management profile may include coolant flow rate, coolant inlet temperature, or ground cooling / heating power. In some examples, the process may include a penalty term for energy consumption of the thermal management system, as reflected in the objective function of the optimal control problem.

[0132] At block 914, the control algorithm 900 factors the trade-off between charging time and thermal management. The algorithm may balance the need to minimize charging time with the requirement to maintain the battery pack 104 within safe temperature limits. In some examples, the process may include penalties for deviation from target SOC and temperature, as well as for energy consumption of the thermal management system, as represented by the weighted terms in the objective function.

[0133] At block 916, a convergence check is performed. The control algorithm 900 evaluates whether the optimization has converged to a solution that satisfies constraints and objectives. If convergence is not achieved, the process proceeds to block 918.

[0134] At block 918, the control algorithm 900 adjusts charging and cooling profiles. The control inputs and state trajectories are updated based on the results of the previous iteration. The control algorithm 900 then returns to the iterative optimization process at block 910, continuing to solve the system dynamics and constraints as defined by the coupled electro-thermal model and the optimal control problem.

[0135] If convergence is achieved, the control algorithm 900 advances to block 920 for final application. The optimized charging and cooling profiles are finalized and prepared for implementation. These profiles correspond to the optimal trajectories for the control inputs (charging current and ground cooling / heating power) and state variables, as determined by the solution to the optimal control problem.

[0136] At block 922, the charging and cooling profiles are applied to the battery pack 104. The charging station controller 134 or other control hardware executes the determined charging current and thermal management strategies, following the optimal profiles computed from the model equations and optimization.

[0137] At block 924, the control algorithm 900 outputs the charging current and cooling rates. These outputs may be used to control the actual battery charging hardware, for further analysis, or for monitoring and logging purposes.

[0138] In some examples, the process may be adapted to support multiple battery packs 104 simultaneously, with independent monitoring and control for each pack. The process may also be configured to operate with alternative thermal management architectures, such as redundant pumps or heaters, and may be implemented in various electric vehicle platforms, including eVTOL aircraft and ground vehicles. FIG. 10: TIME HISTORY OF NORMALIZED CHARGING CURRENT, CELL VOLTAGE, AND ANODE PLATING OVERPOTENTIAL

[0139] FIG. 10 is a graphical diagram 1000showing a time-based view of the evolution of normalized charging current, cell voltage, and anode plating overpotential during a charging cycle for a battery cell, according to some examples.

[0140] The diagram 1000 presents two plots aligned along a normalized time axis. The upper plot displays the normalized charging current profile, while the lower plot shows the cell terminal voltage and the anode plating overpotential. The charging process is divided into three stages: constant current (CC), constant plating overpotential (CPo), and constant voltage (CV).

[0141] During the CC stage, the charging current remains at a maximum value, as permitted by system constraints. In some examples, this current is determined by the maximum allowable C-rate for the cell, which may be set based on thermal or system-level considerations. The cell terminal voltage increases as the state-of-charge (SOC) rises. The anode plating overpotential decreases as the cell approaches higher SOC.

[0142] As the process transitions to the CPo stage, the charging current is reduced to maintain the anode plating overpotential above a minimum threshold. In some examples, the minimum plating overpotential ^^^^minis set to 20 mV. The current in this stage is governed by a constraint derived from an electrochemical model, which may be implemented using a pseudo-2D (P2D) model or a lookup table parameterized by cell temperature and terminal voltage. The cell voltage continues to rise, but at a slower rate, as the current is actively regulated to prevent lithium plating.

[0143] In the CV stage, the cell terminal voltage is held at a maximum allowable value, and the charging current continues to decrease as the cell approaches full charge. The anode plating overpotential may increase slightly as the current tapers off. The transition between stages is determined by the intersection of the current, voltage, and plating overpotential constraints.

[0144] The lower plot shows the cell terminal voltage trajectory, which increases during the CC and CPo stages and plateaus during the CV stage. The anode plating overpotential is plotted as a separate curve, decreasing during the CC stage, stabilizing during the CPo stage, and then varying as the current is reduced in the CV stage.

[0145] In some examples, the charging protocol is implemented using a control algorithm 900 that references a lookup look-up table 516 of plating-free charging currents as a function of cell temperature and terminal voltage. The lookup look-up table 516 may be generated offline using a detailed electrochemical model or by three-electrode measurements. The use of a voltage-based lookup table may provide robustness against SOC estimation errors and cell aging effects.

[0146] The initial SOC for the charging cycle is set to 10%. The cell temperature is maintained at 40°C throughout the process. The minimum plating overpotential constraint is enforced to reduce the risk of lithium plating, which may lead to internal short circuits and accelerated degradation.

[0147] Alternative implementations may use different values for the minimum plating overpotential, initial SOC, or cell temperature. The charging current profile may be furtheradjusted based on additional constraints, such as maximum cell temperature, maximum terminal voltage, or system-level power limitations.

[0148] The diagram 1000 illustrates the dynamic adjustment of charging current in response to electrochemical and thermal constraints, as well as the interaction between electrical and thermal states during the charging process. The approach may be used to inform the design of charging algorithms for electric vehicle battery packs, including those used in electric vertical takeoff and landing (eVTOL) aircraft. FIG. 11: NORMALIZED PLATING-FREE CHARGING CURRENT LOOKUP TABLE

[0149] FIG. 11 is a graphical diagram 1100 showing a detailed view of a normalized plating-free charging current look-up table 516 for a battery cell, according to some examples.

[0150] The look-up table 516 provides charging current limits based on terminal voltage and temperature. The look-up table 516 is generated using a pseudo-2D electrochemical model and a proportional-PI controller. In some examples, the lookup look-up table 516 is constructed with a starting state-of-charge (SOC) of 10% and a minimum plating overpotential ^^^^minof 20 mV. The lookup table encodes the maximum permissible charging current that prevents lithium plating across a range of cell temperatures and terminal voltages.

[0151] The process for generating the look-up table 516 begins with the application of a detailed electrochemical model. The model simulates the internal dynamics of a battery cell, including concentration gradients, reaction kinetics, and the evolution of the anode plating overpotential ^^^^^^^^. The plating overpotential ^^^^^^^^is defined as the difference between the anode potential and the equilibrium potential. Lithium plating is thermodynamically favorable when ^^^^^^^^falls below zero. To prevent plating, the charging current is constrainedsuch that ^^^^^^^^ ≥ ^^^^min

[0152] A PI controller is implemented within the electrochemical model to regulate the charging current. The PI controller dynamically adjusts the current to maintain ^^^^^^^^t or above ^^^^minthroughout the charging process. The resulting charging current profile consists of three stages: a constant current (CC) stage, a constant plating overpotential (CPo) stage, and a constant voltage (CV) stage. During the CC stage, the current is set to the maximum value permitted by system-level constraints, such as thermal limits. In the CPo stage, the current is reduced to maintain the plating overpotential at the threshold. In the CV stage, the terminal voltage is held constant and the current tapers as the cell approaches full charge.

[0153] The look-up table 516 may be constructed by repeating the PI-controlled charging simulation at various temperatures and terminal voltages. For each temperature, the maximum plating-free current is determined as a function of terminal voltage. The resulting data is normalized to the maximum allowable current at each temperature. The look-up table 516 includes curves for a range of temperatures, such as 10°C, 15°C, 20°C, 25°C, 30°C, 35°C, and 40°C. Higher temperatures generally permit higher charging currents due to increased electrolyte diffusivity and reduced polarization.

[0154] In some examples, the look-up table 516 is parameterized by terminal voltage and temperature, rather than state-of-charge. This approach may reduce sensitivity to SOC estimation errors and may provide robustness against cell aging. The use of terminal voltage as an input allows direct measurement and avoids inaccuracies associated with SOC estimation, especially as the battery ages. The look-up table 516 may be updated based on laboratory characterization, in-field measurements, or adaptive algorithms.

[0155] The normalized current values in the look-up table 516 are dimensionless and may be scaled to the specific battery configuration in use. The look-up table 516 may be stored in a memory 514 and accessed by charge control algorithms 320 or a charging station controller 134. The look-up table 516 may be used as a constraint in an optimal control problem for battery charging, ensuring that the applied current does not exceed the plating- free limit for the measured cell temperature and terminal voltage.

[0156] In some examples, the look-up table 516 may be generated using three-electrode measurements to directly measure the negative electrode potential during charging. In other examples, a virtual sensor may estimate the negative electrode potential in real time using a model of the cell. The look-up table 516 may be extended to include additional dimensions, such as initial SOC or cell age, if required for specific applications.

[0157] The look-up table 516 shown in FIG. 11 may be used in conjunction with a coupled electro-thermal model 324 and a multi-node thermal model 328 to inform charging strategies that balance charging time, safety, and battery longevity. The look-up table 516 may be integrated into a battery management system 508 or an energy management system 510 for real-time control of charging operations. FIG. 12: BATTERY CELL AND COLD PLATE THERMAL NETWORK

[0158] FIG. 12 is a schematic diagram 1200 showing a detailed view of a battery cell and the associated cooling system within a thermal network model, according to some examples.

[0159] A battery cell is positioned between layers of insulation. The insulation may reduce heat transfer between the battery cell and the external environment. A cold plate is arrangedbetween two battery cells. The cold plate may be constructed from a material with high thermal conductivity, such as aluminum or copper alloys, to facilitate heat transfer. The cold plate is thermally coupled to a coolant flow path, which may be part of a larger thermal management system.

[0160] The thermal network is represented by a series of thermal nodes, each associated with a heat capacity. The first node, Cp,1, may correspond to the insulated surface of the battery cell. The second node, Cp,2, and the third node, Cp,3, may represent internal regions of the battery cell where heat generation occurs during electrochemical reactions. The fourth node, Cp,4, may correspond to the surface of the battery cell in contact with the cold plate. The coolant node, mCp,c, represents the thermal mass of the coolant circulating within the cold plate.

[0161] Thermal resistances R12, R23, R34, and R4c are arranged between adjacent nodes. R12 models the resistance to heat flow between Cp,1 and Cp,2. R23 models the resistance between Cp,2 and Cp,3. R34 models the resistance between Cp,3 and Cp,4. R4c models the resistance between Cp,4 and mCp,c. These thermal resistances may be determined by the physical properties and geometry of the battery cell, cold plate, and coolant interface.

[0162] The thermal network may be used to simulate the temperature distribution across the battery cell and the cold plate during charging or discharging. In some examples, heat generation is distributed between Cp,2 and Cp,3 according to the local heat capacity fraction. The coolant in the cold plate may be actively circulated by a pump to extract heat from the battery cell. The temperature of the coolant may be regulated by a chiller or other thermal conditioning system.

[0163] The configuration shown in FIG.12 may be adapted to include additional thermal nodes or alternative arrangements of insulation and cold plates. In some examples, the coolant flow path may be configured for parallel or series flow, depending on the thermal management requirements. The thermal network may be integrated with a coupled electro- thermal model to inform charging and thermal management strategies for electric vehicle battery packs. FIG. 13: CHARGING AND THERMAL MANAGEMENT SOLUTION FOR BATTERY PACK

[0164] FIG. 13 is a graphical diagram 1300 showing a time-based view of a charging and thermal management process for a battery pack 600, according to some examples.

[0165] The diagram presents the results of a coupled electro-thermal optimal control problem for charging a battery pack 600 from a state-of-charge (SOC) of 30% to 80% andcooling from a normalized temperature of 0.90 to 0.45. The process is governed by a set of nonlinear state-space equations that describe the electrical and thermal dynamics of the battery system. The system dynamics may be defined by equations such as: •, where ^^^^ is the SOC, ^^^^^^^^is the coulombic efficiency, ^^^^appis the applied current, and ^^^^ is the nominal capacity. ^^^^^^^^•^^^^ = ^^^^ ^^^^^^^ −^^^^^^^^, where ^^^^^^^^is the voltage across the i-th RC pair, ^^^^^^^^is the^resistance, and ^^^^^^^^τi is the time constant. •where ^^^^^^^^ is the temperature at node i, ^^^^^^^^^^^^ is thethermal resistance, and ^^^^genis the heat generation term.

[0166] The objective function for the control problem may include terms for minimizing the final time, penalizing deviations from target SOC and temperature, penalizing cooling / heating energy, and smoothing the charging current profile. The objective function may be expressed as:^^^^4 ∑^^^^−1^^^^=1 (^^^^app,^^^^+1 − ^^^^app,^^^^)2, where ^^^^^^^^ is the final time, ^^^^^^^^ and ^^^^^^^^ are slackvariables for SOC and temperature, ^^^^^^^^is the ground cooling / heating power, and ^^^^1,^^^^2,^^^^3,^^^^4are scalar weights.

[0167] The constraints for the control problem may include: •Terminal voltage constraint: ^^^^^^^^ ≤ ^^^m^ axVt≤Vmax, wher^^^^^^^^e Vt is the terminal voltageis the maximum allowable voltage.•Plating-free current constraint: ^^^^app ≤ ^^^^pf(^^^^min,^^^^^^^^), where ^^^^pf is the plating-freecurrent limit as a function of minimum cell temperature and terminal voltage, derived from a lookup table 516. •Temperature constraints: ^^^^^^^^,min ≤ ^^^^^^^^ ≤ ^^^^^^^^,max for each thermal node.• Coolant temperature constraints: ^^^^^^^^,min ≤ ^^^^^^^^ ≤ ^^^^^^^^,max• Control input constraints: ^^^^min ≤ ^^^^^^^^ and rate-of-change constraintsfor ^^^^appand ^^^^^^^^.

[0168] The top plot in FIG. 13 shows the evolution of terminal voltage and charging current over normalized time. The charging current initially follows the maximum plating- free current limit, which is determined by a look-up table 516 parameterized by cell temperature and terminal voltage. As the terminal voltage approaches the upper constraint, the charging current is reduced to prevent exceeding ^^^m^ax.

[0169] The second plot shows the SOC trajectory, which increases from 30% to 80% in accordance with the applied current and the SOC dynamic equation. The SOC reaches the target value at the end of the charging process, as enforced by the terminal constraint.

[0170] The third plot displays the cell temperature profile. The cell temperature is initially allowed to rise to near the upper limit to reduce cell impedance and polarization growth, which may enable higher charging currents. As the process continues, the cell temperature is gradually reduced to reach the target value of 0.45 at the end of the cycle. The cell temperature dynamics are governed by the multi-node thermal model 402, which may include four thermal nodes and a coolant node, with heat generation distributed among the central nodes.

[0171] The fourth plot shows the coolant temperature, which is managed by the ground cooling system. The coolant temperature is initially increased to promote efficient charging, then reduced to facilitate cooling of the battery pack 600 as the process approaches the target temperature.

[0172] The bottom plot presents the ground cooling / heating power ^^^^^^^^, which is the control input for the thermal management system. The profile of ^^^^^^^^reflects the need to balance rapid cooling with energy efficiency, as penalized in the objective function. The cooling power is increased toward the end of the process to achieve the target cell temperature.

[0173] The red shaded regions in the plots indicate intervals where constraints are active. For example, the terminal voltage constraint and the plating-free current constraint may become active at different stages of the process, as reflected in the charging current and voltage profiles.

[0174] In some examples, the optimal control problem is solved using a direct collocation approach, which discretizes the time interval into segments and approximates state trajectories using polynomial interpolation at collocation points. The solution variables may include the states at collocation and discretization points, control inputs, final time, and slack variables. The constraints are enforced at the discretization points, and continuity constraints are applied between segments.

[0175] Alternative implementations may use different numbers of thermal nodes, alternative parameterizations for the lookup table 516, or different numerical optimization techniques. The system may be adapted for different battery chemistries, pack configurations, or cooling system architectures. The approach may be used for real-time control or for offline planning of charging and thermal management strategies. FIG. 14: CHARGING AND COOLING TRAJECTORIES FOR SOC 30% TO 60% AND NORMALIZED TEMPERATURE 0.90 TO 0.65

[0176] FIG. 14 is a graphical diagram 1400 showing a time-based view of a charging and thermal management process for a battery pack 104, according to some examples. The diagram presents the results of a coupled electro-thermal optimal control problem for charging a battery pack 104 from a state-of-charge (SOC) of 30% to 60% and cooling from a normalized temperature of 0.90 to 0.65. The process is governed by a set of nonlinear state-space equations that describe the electrical and thermal dynamics of the battery system

[0177] The top plot in FIG. 14 shows the evolution of terminal voltage and charging current over normalized time. The charging current may initially follow the maximum plating-free current limit, which is determined by look-up tables 330 parameterized by cell temperature and terminal voltage. The terminal voltage may increase as the SOC rises,subject to the constraint ^^^^^^^^ ≤ ^^^m^ ax where ^^^^^^^^ is the terminal voltage and ^^^m^ axis the maximumallowable voltage. The charging current may be reduced as the SOC approaches the target value, reflecting the reduced capacity for current input at higher SOC levels.

[0178] The second plot shows the SOC trajectory, which increases from 30% to 60% in accordance with the applied current and the SOC dynamic equation:where ^^^^ is the SOC, ^^^^^^^^is the coulombic efficiency, ^^^^appis the applied current, and ^^^^C is the nominal capacity.

[0179] The third plot displays the cell temperature profile, including the minimum, maximum, and average cell temperatures. The cell temperature may be regulated near the target value of 0.65 during most of the charging cycle. The cell temperature dynamics are governed by the multi-node thermal model 402:where ^^^^^^^^is the temperature at node ^^^^i, ^^^^^^^^^^^^is the thermal resistance 410 between nodes ^^^^ and ^^^^, and ^^^^genis the heat generation term. The heat generation term may include both irreversible (ohmic) and reversible (entropic) heating, and may be distributed among the central nodes of the battery.

[0180] The fourth plot shows the coolant temperature, which is managed by the ground cooling system 412. The coolant temperature may be reduced at the start of the process to facilitate cooling of the battery pack 104, and may be regulated near the target temperature of 0.65 as the process continues. The coolant temperature dynamics are described by:where ^^^^^^^^is the coolant temperature, ^̇^^^^^^^is the coolant mass flow rate, ^^^^^^^^,inis the inlet coolant temperature, and ^^^^4^^^^is the thermal resistance between the coolant and the battery surface.

[0181] The bottom plot presents the ground cooling / heating power ^^^^^^^^, which is the control input for the thermal management system 412. The cooling power may be increased at the start of the process to reduce the battery temperature, and may be adjusted dynamically to maintain the target temperature. The cooling power is governed by:where ^^^^^^^^represents the heating or cooling input from the ground system.

[0182] The shaded regions in the plots indicate intervals where constraints are active. Theplating-free current constraint ^^^^app ≤ ^^^^pf(^^^^min,^^^^^^^^) may be active during the initial phase ofcharging. The cell temperature constraints ^^^^^^^^,min ≤ ^^^^^^^^ ≤ ^^^^^^^^,maxmay be enforced throughoutthe cycle to prevent overheating or overcooling. The terminal voltage constraint ^^^^^^^^≤ ^^^m^axmay become relevant as the SOC approaches the target.

[0183] The underlying optimal control problem is formulated to minimize the final time ^^^^^^^^tf while penalizing deviations from the target SOC and temperature, as well as excessive cooling power. The objective function may be expressed as: minimizewhere ^^^^^^^^and ^^^^^^^^are slack variables for SOC and temperature, and ^^^^1,^^^^2,^^^^3,^^^^4are scalar weights.

[0184] The optimization problem may be solved using a direct collocation approach, which discretizes the time interval into segments and approximates state trajectories using polynomial interpolation at collocation points. The solution variables may include the states at collocation and discretization points, control inputs, final time, and slack variables. The constraints are enforced at the discretization points, and continuity constraints are applied between segments.

[0185] In some examples, the process may be adapted for different battery chemistries, pack configurations, or cooling system architectures. The approach may be used for real- time control or for offline planning of charging and thermal management strategies. FIG. 15: CHARGING AND COOLING TRAJECTORIES FOR SOC 65% TO 95% AND NORMALIZED TEMPERATURE 0.82 TO 0.45

[0186] FIG. 15 is a graphical diagram showing a time-based view of a charging and thermal management process for a battery pack 104, according to some examples.

[0187] The diagram presents the results of a coupled electro-thermal optimal control problem for charging a battery pack 104 from a state-of-charge (SOC) of 65% to 95% and cooling from a normalized temperature of 0.82 to 0.45. The process is governed by a set of nonlinear state-space equations that describe the electrical and thermal dynamics of the battery system. The system dynamics may be defined by equations such as: ^^^^^^^^ ^^^^ • =^^^^^^^^app, where ^^^^ is^^^^^^^^ ^^^^ SOC, ^^^^^^^^is the coulombic efficiency, ^^^^appis the applied current, and ^^^^is the nominal capacity. •where ^^^^ is the voltage across the i-th RC pair, ^^^^ is the resistanc ^^^ ^^^^^^^^ ^e, ^^^^^^^^^^^^and ^^^^^^^^is the time constant. • ^^^^^^^^,^^^^ + ^^^^genwhere ^^^^^^^^ is the temperature at node i, ^^^^^^^^^^^^ is the thermalresistance, and ^^^^genis the heat generation term.

[0188] The objective function for the control problem may include terms for minimizing the final time, penalizing deviations from target SOC and temperature, penalizing cooling / heating energy, and smoothing the charging current profile. The objective function may be expressed as:^^^^ ^^^^app,^^^^)2, where ^^^^^^^^is the final time, ^^^^^^^^and ^^^^^^^^are slack variables for SOC and temperature, ^^^^^^^^is the ground cooling / heating power, and ^^^^1,^^^^2,^^^^3,^^^^4are scalar weights.

[0189] The constraints for the control problem may include: •Terminal voltage constraint: ^^^^ where ^^^^ is the terminal voltagethe maximum allowable voltage. •Plating-free current constraint: ^^^^app ≤ ^^^^pf(^^^^min,^^^^^^^^), where ^^^^pf is the plating-freecurrent limit as a function of minimum cell temperature and terminal voltage, derived from look-up tables 330. •Temperature constraints:≤ ^^^^^^^^ ≤ ^^^^^^^^,max for each thermal• Coolant temperature constraints: ^^^^^^^^,min ≤ ^^^^^^^^ ≤ ^^^^^^^^,max.• Control input constraints: ^^^^ ≤ ^^^^^^^^and rate-of-changefor ^^^^appand ^^^^^^^^.

[0190] The top plot in FIG. 15 shows the evolution of terminal voltage and charging current over normalized time. The charging current initially follows the maximum plating- free current limit, which is determined by a lookup table 330 parameterized by cell temperature and terminal voltage. As the terminal voltage approaches the upper constraint, the charging current is reduced to prevent exceeding ^^^m^ax.

[0191] The second plot shows the SOC trajectory, which increases from 65% to 95% in accordance with the applied current and the SOC dynamic equation. The SOC reaches the target value at the end of the charging process, as enforced by the terminal constraint.

[0192] The third plot displays the cell temperature profile, including the minimum, maximum, and average cell temperatures. The cell temperature is initially allowed to rise to near the upper limit to reduce cell impedance and polarization growth, which may enable higher charging currents. As the process continues, the cell temperature is gradually reduced to reach the target value of 0.45 at the end of the cycle. The cell temperature dynamics are governed by the multi-node thermal model 402, which may include four thermal nodes and a coolant node, with heat generation distributed among the central nodes.

[0193] The fourth plot shows the coolant temperature, which is managed by the ground cooling system. The coolant temperature is initially increased to promote efficient charging,then reduced to facilitate cooling of the battery pack 104 as the process approaches the target temperature.

[0194] The bottom plot presents the ground cooling / heating power ^^^^^^^^, which is the control input for the thermal management system. The profile of ^^^^^^^^reflects the need to balance rapid cooling with energy efficiency, as penalized in the objective function. The cooling power is increased toward the end of the process to achieve the target cell temperature.

[0195] The red shaded regions in the plots indicate intervals where constraints are active. For example, the terminal voltage constraint and the plating-free current constraint may become active at different stages of the process, as reflected in the charging current and voltage profiles.

[0196] In some examples, the optimal control problem is solved using a direct collocation approach, which discretizes the time interval into segments and approximates state trajectories using polynomial interpolation at collocation points. The solution variables may include the states at collocation and discretization points, control inputs, final time, and slack variables. The constraints are enforced at the discretization points, and continuity constraints are applied between segments.

[0197] Alternative implementations may use different numbers of thermal nodes, alternative parameterizations for the lookup table 330, or different numerical optimization techniques. The system may be adapted for different battery chemistries, pack configurations, or cooling system architectures. The approach may be used for real-time control or for offline planning of charging and thermal management strategies. FIG. 16: PLAN VIEW OF VTOL AIRCRAFT ARCHITECTURE

[0198] FIG. 16 is a projection diagram showing a plan view of an aircraft 1600 configured as a vertical takeoff and landing (VTOL) electric vehicle, according to some examples.

[0199] The aircraft 1600 includes a fuselage 1602, which provides the primary structural body for housing avionics, payload, and flight control systems. The fuselage 1602 may be constructed from composite materials or metallic alloys to achieve a balance of strength and weight.

[0200] A pair of wings 1604 are attached to the fuselage 1602. The wings 1604 are configured to generate aerodynamic lift during forward flight. In some examples, the wings 1604 may include internal cavities or structural supports for integrating additional subsystems, such as wing battery packs 1606 or wiring harnesses.

[0201] An empennage 1608 is positioned at the aft section of the fuselage 1602. The empennage 1608 may include horizontal and vertical stabilizers for providing pitch and yaw stability. Control surfaces on the empennage 1608 may be actuated by electric or hydraulic actuators.

[0202] The aircraft 1600 is equipped with propulsion systems 1610. The propulsion systems 1610 are distributed across the airframe and may include multiple rotor assemblies 1612. Each rotor assembly 1612 is mounted within a nacelle 1614. The rotor assemblies 1612 may be configured for tilting to enable both vertical lift and forward thrust, supporting VTOL and cruise flight modes.

[0203] Nacelle battery packs 1616 are integrated within the nacelles 1614. In some examples, the nacelle battery packs 1616 are located in inboard nacelles 1618, which are positioned closer to the fuselage 1602. The nacelle battery packs 1616 supply electrical energy to the propulsion systems 1610, including the rotor assemblies 1612. The battery packs may be constructed from large-format pouch cells or prismatic cells, and may include integrated battery management systems for monitoring voltage, current, and temperature.

[0204] Wing battery packs 1606 are embedded within the wings 1604. The wing battery packs 1606 may be arranged in a distributed manner to optimize weight distribution and structural integrity. The wing battery packs 1606 may be electrically isolated or interconnected, depending on the aircraft’s power architecture. In some examples, the wing battery packs 1606 may be thermally managed using integrated cold plates or coolant flow channels.

[0205] The inboard nacelles 1618 provide structural support for the nacelle battery packs 1616 and the associated propulsion systems 1610. The inboard nacelles 1618 may be designed to accommodate electrical cabling, cooling lines, and control wiring for the rotor assemblies 1612.

[0206] The aircraft 1600 may further include additional subsystems not shown in the plan view, such as landing gear, avionics bays, and environmental control systems. The electrical architecture of the aircraft 1600 may support redundant power distribution, fault-tolerant control, and real-time monitoring of energy storage and propulsion components.

[0207] In some examples, the aircraft 1600 may be configured to interface with ground support equipment for charging and thermal management of the nacelle battery packs 1616 and wing battery packs 1606. The battery packs may include charge ports and thermal interfaces to support rapid turnaround between flight operations.

[0208] Alternative configurations may include different numbers or arrangements of rotor assemblies 1612, nacelles 1614, or battery packs, depending on mission requirements and vehicle size. The modular arrangement of the propulsion systems 1610 and energy storage components may support scalability and adaptation to various VTOL aircraft platforms. FIG 11: ENERGY STORAGE SYSTEM 1700

[0209] FIG. 17 is a schematic view of an aircraft energy storage system 1700 according to some examples, which may be managed by the energy management system (EMS) 510. As shown, the energy storage system 1700 includes one or more battery packs 1702. Each battery pack 1702 may include one or more battery modules 1704, which in turn may comprise a number of cells 1706.

[0210] Typically associated with a battery pack 1702 are one or more propulsion systems 1610, a battery mate 1708 for connecting it to the energy storage system 1700, a burst membrane 1710 as part of a venting system, a fluid circulation system 1712 for cooling, and power electronics 1714 for regulating delivery of electrical power (from the battery during operation and to the battery during charging) and to provide integration of the battery pack 1702 with the electronic infrastructure of the energy storage system 1700. As discussed in more detail below, the propulsion systems 1610 may comprise multiple rotor assemblies.

[0211] The electronic infrastructure and the power electronics 1714 can additionally or alternately function to integrate the battery packs 1702 into the energy storage system 1700 of the aircraft. The electronic infrastructure can include a battery management system (BMS) 508, power electronics (HV architecture, power components, and so forth), LV architecture (e.g., vehicle wire harness, data connections, and so forth), and / or any other suitable components. The electronic infrastructure can include inter-module electrical connections, which can transmit power and / or data between battery packs and / or modules. Inter-modules can include bulkhead connections, bus bars, wire harnessing, and / or any other suitable components.

[0212] The battery packs 1702 function to store electrochemical energy in a rechargeable manner for supply to the propulsion systems 1610. Battery packs 1702 can be arranged and / or distributed around the aircraft in any suitable manner. Battery packs can be arranged within wings (e.g., inside of an airfoil cavity), inside nacelles, and / or in any other suitable location on the aircraft. In a specific example, the energy storage system 1700 includes a first battery pack within an inboard portion of a left wing and a second battery pack within an inboard portion of a right wing. In a second specific example, the system includes a first battery pack within an inboard nacelle of a left wing and a second battery pack within aninboard nacelle of a right wing. Battery packs 1702 may include a plurality of battery modules 1704.

[0213] The energy storage system 1700 includes a cooling system (e.g., fluid circulation system 1712) that functions to circulate a working fluid within the battery pack 1702 to remove heat generated by the battery pack 1702 during operation or charging. Battery cells 1706, battery module 1704 and / or battery packs 1702 can be fluidly connected by the cooling system in series and / or parallel in any suitable manner. FIG. 18: ELECTRICAL ARCHITECTURE FOR AIRCRAFT POWER AND DATA DISTRIBUTION

[0214] FIG. 18 is a system diagram showing a detailed view of an electrical architecture 1802 for an aircraft 1804, according to some examples.

[0215] The electrical architecture 1802 includes an energy storage system 1806, multiple flight devices 1808, multiple flight computers 1810, and a network 1812 comprising switches 1814. The energy storage system 1806 may be configured to supply electrical power to the flight devices 1808 and flight computers 1810. In some examples, the energy storage system 1806 may include one or more battery packs, as described in FIG. 17, where each battery pack may comprise a plurality of battery modules and cells. The energy storage system 1806 may be arranged in a distributed manner within the aircraft 1804, such as in nacelles or wings, as shown in FIG. 16.

[0216] The flight devices 1808 may include actuators, control surfaces, propulsion systems, sensors, and other electrically powered components required for aircraft operation. The flight devices 1808 may be connected to the network 1812 to receive power and data signals. In some examples, the propulsion systems may include rotor assemblies, as described in FIG. 16, and may be powered directly by the energy storage system 1806.

[0217] The flight computers 1810 may be configured to execute control algorithms, process sensor data, and manage the operation of the flight devices 1808. The flight computers 1810 may interface with the energy storage system 1806 to monitor battery state, voltage, current, and temperature. In some examples, the flight computers 1810 may implement battery models and energy management strategies, as described in FIG. 5, to optimize power usage and maintain operational constraints.

[0218] The network 1812 may provide both power distribution and data communication pathways between the energy storage system 1806, flight devices 1808, and flight computers 1810. The network 1812 may include redundant paths and may support bothwired and wireless communication protocols. The network 1812 may be configured to support real-time data exchange for flight control, state monitoring, and fault detection.

[0219] Switches 1814 may be integrated within the network 1812 to enable selective routing of power and data. The switches 1814 may be configured to isolate faults, reconfigure network topology, or support maintenance operations. In some examples, the switches 1814 may be solid-state devices or electromechanical relays.

[0220] The electrical architecture 1802 may be designed to provide redundancy and fault tolerance. Multiple parallel paths may be established between the energy storage system 1806 and the flight devices 1808. The network 1812 may be segmented to allow continued operation in the event of a failure in one segment.

[0221] In some examples, the energy storage system 1806 may be monitored and controlled by the flight computers 1810 using data received over the network 1812. The flight computers 1810 may receive sensor data from the flight devices 1808 and may issue control commands to adjust actuator positions, propulsion system output, or other operational parameters.

[0222] The electrical architecture 1802 may be adapted to support various aircraft configurations. The number and arrangement of flight devices 1808, flight computers 1810, and switches 1814 may be varied based on aircraft size, mission requirements, or redundancy targets. The energy storage system 1806 may be implemented using different battery chemistries, cell formats, or modular arrangements.

[0223] In some examples, the network 1812 may also support communication with external systems, such as ground support equipment or remote monitoring centers, as described in FIG. 1. The electrical architecture 1802 may facilitate firmware updates, diagnostics, and data logging for maintenance and operational analysis. FIG. 19: COMPUTER SYSTEM ARCHITECTURE FOR BATTERY MANAGEMENT AND CONTROL

[0224] FIG. 19 is a system diagram showing a detailed view of a computer system 1900 configured to execute instructions for battery management, charging control, and data processing, according to some examples.

[0225] The computer system 1900 may represent a computing apparatus used as an energy management system computer 512, a charging station controller 134, or a server within a monitoring and control center 138. The computer system 1900 includes processors 1902, which may comprise a processor 1904 and a processor 1906. The processors 1902 areconfigured to execute instructions 1908 stored in memory 1910 or in a storage unit 1912. The instructions 1908 may include algorithms for battery charging control, thermal management, and optimization routines, such as those described for charge control algorithms 320 and battery models 322 in earlier figures.

[0226] The memory 1910 includes a main memory 1914, a static memory 1916, and a storage unit 1912. The main memory 1914 and static memory 1916 may store executable instructions 1908, look-up tables 516, and data structures for real-time and historical data processing. The storage unit 1912 may include a machine-readable medium 1918, which may store persistent data such as battery model parameters, charging profiles, and operational logs. The bus 1920 interconnects the processors 1902, memory 1910, and I / O components 1922, enabling data transfer and coordination between subsystems.

[0227] The I / O components 1922 may include output components 1924, such as visual displays, acoustic devices, and haptic feedback mechanisms. These output components 1924 may provide system status, charging progress, or diagnostic information to operators or external systems. Input components 1926 may include alphanumeric input devices, point- based input devices, tactile sensors, and audio input devices, allowing for user configuration, command input, or system calibration.

[0228] The computer system 1900 may further include biometric components 1928 for user identification or access control, motion components 1930 for detecting acceleration, rotation, or orientation, and environmental components 1932 for monitoring ambient conditions such as temperature, pressure, or acoustic environment. Position components 1934 may provide location, altitude, or orientation data, which may be used in conjunction with energy management system 510 or for integration with aircraft navigation systems.

[0229] Communication components 1936 may provide connectivity to a network 1938 or to external devices 1940. The communication components 1936 may support wired, wireless, cellular, near-field, Bluetooth, or Wi-Fi protocols. The coupling 1942 and coupling 1944 may facilitate data exchange with other systems, such as a data offload server 136, a monitoring and control center 138, or ground support equipment 102. The network 1938 may support data transfer for firmware updates, remote diagnostics, or operational data logging.

[0230] The computer system 1900 may execute instructions 1908 that implement battery models 322, including a coupled electro-thermal model 324, an equivalent circuit model 326, and a multi-node thermal model 328. The instructions 1908 may also implement optimization algorithms for charging and thermal management, as described in the contextof direct collocation or model predictive control. The system may access look-up tables 516 for parameterizing model elements based on state-of-charge, temperature, current, or cell age.

[0231] In some examples, the computer system 1900 may be configured to interface with sensors distributed throughout a battery pack 104, a thermal management system 118, or a charging station 202. The system may process real-time sensor data, execute control algorithms, and transmit control signals to actuators or power electronics. The computer system 1900 may also log operational data for predictive maintenance, diagnostics, or regulatory compliance.

[0232] Alternative configurations may include additional processors, memory modules, or I / O components, depending on the deployment environment. The computer system 1900 may be implemented as a standalone unit, as part of a distributed control network, or as a virtualized instance within a cloud-based infrastructure. The architecture may support integration with external monitoring systems, remote data repositories, or other aircraft subsystems. EXAMPLES

[0233] Example 1 is a method to manage a charging process of a battery pack for an electric vehicle, the method comprising: initiating a charging cycle for the battery pack; actively managing temperature of the battery pack during the charging cycle by flowing a temperature-controlled coolant through a thermal management system integrated with the battery pack; monitoring state-of-charge (SOC) and temperature of the battery pack; adjusting a charging current and the temperature of the temperature-controlled coolant in real-time based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation; and terminating the charging cycle when the target SOC and the target temperature range are achieved within predefined safety and degradation constraints to prepare the electric vehicle for subsequent use.

[0234] In Example 2, the subject matter of Example 1 includes, wherein the charging current is adjusted based on a constraint derived from an electrochemical model of the battery pack to prevent degradation mechanisms.

[0235] In Example 3, the subject matter of Example 2 includes, wherein the constraint is determined by maintaining a specific overpotential within battery cells of the battery pack above a predetermined threshold.

[0236] In Example 4, the subject matter of Examples 1–3 includes, wherein the temperature-controlled coolant is provided by an external cooling system capable of adjusting a coolant flow rate based on the battery pack's temperature.

[0237] In Example 5, the subject matter of Example 4 includes, wherein the external cooling system includes a mechanism for rapid temperature adjustment to meet the target temperature range quickly.

[0238] In Example 6, the subject matter of Examples 1–5 includes, wherein the coupled electro-thermal model includes a thermal network model that simulates temperature distribution within the battery pack.

[0239] In Example 7, the subject matter of Example 6 includes, wherein the thermal network model comprises multiple nodes that represent various components and regions within the battery pack.

[0240] In Example 8, the subject matter of Examples 1–7 includes, wherein the real-time adjustments of the charging current and coolant temperature are based on an optimization algorithm that minimizes charging time.

[0241] In Example 9, the subject matter of Example 8 includes, wherein the optimization algorithm accounts for constraints on voltage and temperature of the battery pack.

[0242] In Example 10, the subject matter of Example 9 includes, wherein the constraints include preventing the battery pack's voltage from exceeding a maximum allowable limit.

[0243] In Example 11, the subject matter of Examples 1–10 includes, wherein the charging cycle includes multiple stages with varying charging currents to optimize the charging process.

[0244] In Example 12, the subject matter of Example 11 includes, wherein the stages of the charging cycle are dynamically adjusted based on the real-time SOC and temperature data from the battery pack.

[0245] In Example 13, the subject matter of Examples 1–12 includes, wherein the termination of the charging cycle is based on reaching the target SOC and temperature range, and meeting safety and operational constraints specific to the electric vehicle.

[0246] In Example 14, the subject matter of Example 13 includes, wherein the safety and operational constraints are determined by electric vehicle's manufacturer specifications.

[0247] In Example 15, the subject matter of Examples 1–14 includes, wherein the charging cycle is optimized to balance charging speed with longevity of the battery pack.

[0248] In Example 16, the subject matter of Example 15 includes, wherein the optimization of the charging cycle includes a penalty function for threshold use of cooling resources.

[0249] In Example 17, the subject matter of Examples 1–16 includes, wherein the method further comprises a diagnostic check of a condition of the battery pack after the charging cycle is completed.

[0250] In Example 18, the subject matter of Examples 1–17 includes, wherein the method includes storing data related to the charging cycle for analysis of the charging process.

[0251] In Example 19, the subject matter of Examples 1–18 includes, wherein the method includes a feedback loop that adjusts charging parameters based on performance of the battery pack during previous charging cycles.

[0252] Example 20 is a method to charge a battery pack of an electric vehicle, the method comprising: accessing a model that predicts behavior of the battery pack during charging; accessing a control problem to achieve a desired state of charge (SOC) and temperature for the battery pack; using at least one processor, solving the control problem to determine a charging strategy; and applying the charging strategy to the battery pack to reach the desired SOC and temperature while adhering to operational constraints.

[0253] In Example 21, the subject matter of Example 20 includes, wherein the model is a coupled electro-thermal model that represents electrical and thermal behavior of at least one battery cell within the battery pack.

[0254] In Example 22, the subject matter of Example 21 includes, wherein the coupled electro-thermal model includes an electrochemical model to establish charging current limits to prevent lithium plating.

[0255] In Example 23, the subject matter of Example 22 includes, wherein the charging current limits are determined by a lookup table correlating cell temperature and terminal voltage with permissible charging currents.

[0256] In Example 24, the subject matter of Examples 20–23 includes, wherein solving the control problem involves a numerical optimization that discretizes the control problem into intervals and uses polynomial functions to approximate state trajectories.

[0257] In Example 25, the subject matter of Example 24 includes, wherein a numerical optimization technique is a direct collocation approach that ensures continuity of the state trajectories across the intervals.

[0258] In Example 26, the subject matter of Examples 20–25 includes, wherein the control problem includes constraints on voltage and temperature of a battery cell of the battery pack to prevent damage during charging.

[0259] In Example 27, the subject matter of Example 26 includes, wherein the constraints are applied at specific intervals to maintain the battery cell within safe operating conditions.

[0260] In Example 28, the subject matter of Examples 20–27 includes, wherein the control problem is structured to reduce time required to charge the battery pack to a predetermined SOC and temperature.

[0261] In Example 29, the subject matter of Example 28 includes, wherein the control problem includes slack variables to allow for flexibility in achieving predetermined SOC and temperature targets.

[0262] In Example 30, the subject matter of Examples 20–29 includes, wherein the charging strategy includes a thermal management profile that regulates the temperature of the battery pack using a cooling system.

[0263] In Example 31, the subject matter of Example 30 includes, wherein the thermal management profile is derived from a model of dynamics of a cooling system.

[0264] In Example 32, the subject matter of Examples 20–31 includes, wherein the charging strategy is initiated with predefined initial conditions based on a state of the battery pack before charging.

[0265] In Example 33, the subject matter of Examples 20–32 includes, wherein the charging strategy includes a charging current profile that is adjusted based on real-time data from the battery pack.

[0266] In Example 34, the subject matter of Example 33 includes, wherein the charging current profile is smoothed to prevent abrupt changes in charging rates.

[0267] In Example 35, the subject matter of Examples 20–34 includes, wherein the charging strategy is adapted dynamically during a charging process to account for variations in responses of the battery pack.

[0268] In Example 36, the subject matter of Examples 20–35 includes, wherein the charging strategy includes a penalty term for energy consumption of a thermal management system to promote energy efficiency.

[0269] Example 37 is a computing apparatus including at least one processor and at least one memory storing instructions configured such that, when executed in cooperation with controlling the at least one processor, the instructions operate the apparatus to perform amethod comprising: initiating a charging cycle for the battery pack; actively managing temperature of the battery pack during the charging cycle by flowing a temperature- controlled coolant through a thermal management system integrated with the battery pack; monitoring state-of-charge (SOC) and temperature of the battery pack; adjusting a charging current and the temperature of the temperature-controlled coolant in real-time based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation; and terminating the charging cycle when the target SOC and the target temperature range are achieved within predefined safety and degradation constraints to prepare the electric vehicle for subsequent use.

[0270] In Example 38, the subject matter of Example 37 includes, wherein the charging current is adjusted based on a constraint derived from an electrochemical model of the battery pack to prevent degradation mechanisms.

[0271] In Example 39, the subject matter of Example 38 includes, wherein the constraint is determined by maintaining a specific overpotential within battery cells of the battery pack above a predetermined threshold.

[0272] In Example 40, the subject matter of Examples 37–39 includes, wherein the temperature-controlled coolant is provided by an external cooling system capable of adjusting a coolant flow rate based on the battery pack's temperature.

[0273] In Example 41, the subject matter of Example 40 includes, wherein the external cooling system includes a mechanism for rapid temperature adjustment to meet the target temperature range quickly.

[0274] In Example 42, the subject matter of Examples 37–41 includes, wherein the coupled electro-thermal model includes a thermal network model that simulates temperature distribution within the battery pack.

[0275] In Example 43, the subject matter of Example 42 includes, wherein the thermal network model comprises multiple nodes that represent various components and regions within the battery pack.

[0276] In Example 44, the subject matter of Examples 37–43 includes, wherein the real- time adjustments of the charging current and coolant temperature are based on an optimization algorithm that minimizes charging time.

[0277] In Example 45, the subject matter of Example 44 includes, wherein the optimization algorithm accounts for constraints on voltage and temperature of the battery pack.

[0278] In Example 46, the subject matter of Example 45 includes, wherein the constraints include preventing the battery pack's voltage from exceeding a maximum allowable limit.

[0279] In Example 47, the subject matter of Examples 37–46 includes, wherein the charging cycle includes multiple stages with varying charging currents to optimize the charging process.

[0280] In Example 48, the subject matter of Example 47 includes, wherein the stages of the charging cycle are dynamically adjusted based on the real-time SOC and temperature data from the battery pack.

[0281] In Example 49, the subject matter of Examples 37–48 includes, wherein the termination of the charging cycle is based on reaching the target SOC and temperature range, and meeting safety and operational constraints specific to the electric vehicle.

[0282] In Example 50, the subject matter of Example 49 includes, wherein the safety and operational constraints are determined by electric vehicle's manufacturer specifications.

[0283] In Example 51, the subject matter of Examples 37–50 includes, wherein the charging cycle is optimized to balance charging speed with longevity of the battery pack.

[0284] In Example 52, the subject matter of Example 51 includes, wherein the optimization of the charging cycle includes a penalty function for threshold use of cooling resources.

[0285] In Example 53, the subject matter of Examples 37–52 includes, wherein the method further comprises a diagnostic check of a condition of the battery pack after the charging cycle is completed.

[0286] In Example 54, the subject matter of Examples 37–53 includes, wherein the method includes storing data related to the charging cycle for analysis of the charging process.

[0287] In Example 55, the subject matter of Examples 37–54 includes, wherein the method includes a feedback loop that adjusts charging parameters based on performance of the battery pack during previous charging cycles.

[0288] Example 56 is a computing apparatus including at least one processor and at least one memory storing instructions configured such that, when executed in cooperation with controlling the at least one processor, the instructions operate the apparatus to perform a method comprising: accessing a model that predicts behavior of the battery pack during charging; accessing a control problem to achieve a desired state of charge (SOC) and temperature for the battery pack; using at least one processor, solving the control problem to determine a charging strategy; and applying the charging strategy to the battery pack to reach the desired SOC and temperature while adhering to operational constraints.

[0289] In Example 57, the subject matter of Example 56 includes, wherein the model is a coupled electro-thermal model that represents electrical and thermal behavior of at least one battery cell within the battery pack.

[0290] In Example 58, the subject matter of Example 57 includes, wherein the coupled electro-thermal model includes an electrochemical model to establish charging current limits to prevent lithium plating.

[0291] In Example 59, the subject matter of Example 58 includes, wherein the charging current limits are determined by a lookup table correlating cell temperature and terminal voltage with permissible charging currents.

[0292] In Example 60, the subject matter of Examples 56–59 includes, wherein solving the control problem involves a numerical optimization that discretizes the control problem into intervals and uses polynomial functions to approximate state trajectories.

[0293] In Example 61, the subject matter of Example 60 includes, wherein a numerical optimization technique is a direct collocation approach that ensures continuity of the state trajectories across the intervals.

[0294] In Example 62, the subject matter of Examples 56–61 includes, wherein the control problem includes constraints on voltage and temperature of a battery cell of the battery pack to prevent damage during charging.

[0295] In Example 63, the subject matter of Example 62 includes, wherein the constraints are applied at specific intervals to maintain the battery cell within safe operating conditions.

[0296] In Example 64, the subject matter of Examples 56–63 includes, wherein the control problem is structured to reduce time required to charge the battery pack to a predetermined SOC and temperature.

[0297] In Example 65, the subject matter of Example 64 includes, wherein the control problem includes slack variables to allow for flexibility in achieving predetermined SOC and temperature targets.

[0298] In Example 66, the subject matter of Examples 56–65 includes, wherein the charging strategy includes a thermal management profile that regulates the temperature of the battery pack using a cooling system.

[0299] In Example 67, the subject matter of Example 66 includes, wherein the thermal management profile is derived from a model of dynamics of a cooling system.

[0300] In Example 68, the subject matter of Examples 56–67 includes, wherein the charging strategy is initiated with predefined initial conditions based on a state of the battery pack before charging.

[0301] In Example 69, the subject matter of Examples 56–68 includes, wherein the charging strategy includes a charging current profile that is adjusted based on real-time data from the battery pack.

[0302] In Example 70, the subject matter of Example 69 includes, wherein the charging current profile is smoothed to prevent abrupt changes in charging rates.

[0303] In Example 71, the subject matter of Examples 56–70 includes, wherein the charging strategy is adapted dynamically during a charging process to account for variations in responses of the battery pack.

[0304] In Example 72, the subject matter of Examples 56–71 includes, wherein the charging strategy includes a penalty term for energy consumption of a thermal management system to promote energy efficiency.

[0305] Example 73 is a computer-readable medium on which computer-executable instructions are stored to implement a method comprising: initiating a charging cycle for the battery pack; actively managing temperature of the battery pack during the charging cycle by flowing a temperature-controlled coolant through a thermal management system integrated with the battery pack; monitoring state-of-charge (SOC) and temperature of the battery pack; adjusting a charging current and the temperature of the temperature-controlled coolant in real-time based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation; and terminating the charging cycle when the target SOC and the target temperature range are achieved within predefined safety and degradation constraints to prepare the electric vehicle for subsequent use.

[0306] Example 74 is a computer-readable medium on which computer-executable instructions are stored to implement a method comprising: accessing a model that predicts behavior of the battery pack during charging; accessing a control problem to achieve a desired state of charge (SOC) and temperature for the battery pack;

[0307] Example 75 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1–74.

[0308] Example 76 is an apparatus comprising means to implement of any of Examples 1– 74.

[0309] Example 77 is a system to implement of any of Examples 1–74.

[0310] Example 78 is a method to implement of any of Examples 1–74.

[0311] Example 79 is a system for managing a charging process of a battery pack for an electric vehicle, comprising: a thermal management system configured to flow a temperature-controlled coolant; a charging station controller configured to: initiate a charging cycle for the battery pack; monitor state-of-charge (SOC) and temperature of the battery pack; adjust a charging current and a coolant temperature of the temperature- controlled coolant based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation; and terminate the charging cycle based on the target SOC and the target temperature range being achieved within predefined constraints.

[0312] Example 80 is the subject matter of Example 79, wherein the charging station controller is further configured to adjust the charging current based on a constraint derived from an electrochemical model of the battery pack to prevent degradation mechanisms.

[0313] Example 81 is the subject matter of any one or more of Examples 79-80, wherein the constraint is determined by maintaining a specific overpotential within battery cells of the battery pack above a predetermined threshold.

[0314] Example 82 is the subject matter of Example 79, wherein the thermal management system comprises an external cooling system capable of adjusting a coolant flow rate based on the temperature of the battery pack.

[0315] Example 83 is the subject matter of any one or more of Examples 79 or 82, wherein the external cooling system includes a temperature adjustment mechanism capable of changing the coolant temperature by at least 5°C within 60 seconds to meet the target temperature range.

[0316] Example 84 is the subject matter of Example 79, wherein the coupled electro- thermal model includes a thermal network model that simulates temperature distribution within the battery pack, the thermal network model comprising a plurality of nodes that represent respective regions within the battery pack.

[0317] Example 85 is the subject matter of any one or more of Examples 79 or 84, wherein the thermal network model comprises at least four thermal nodes representing different regions within each battery cell of the battery pack.

[0318] Example 86 is the subject matter of any one or more of Examples 79, 84-85, wherein the at least four thermal nodes include core nodes with heat generation and surface nodes without heat generation.

[0319] Example 87 is the subject matter of any one or more of Examples 79, 84-86, wherein heat generation is distributed between the core nodes according to heat capacity fractions.

[0320] Example 88 is the subject matter of Example 79, wherein the charging station controller is configured to solve an optimal control problem to determine a charging strategy.

[0321] Example 89 is the subject matter of any one or more of Examples 79 or 88, wherein the optimal control problem includes an objective function that minimizes charging time while penalizing energy consumption of the thermal management system.

[0322] Example 90 is the subject matter of any one or more of Examples 79, 88-89, wherein the objective function further includes a penalty term for smoothing the charging current profile.

[0323] Example 91 is the subject matter of any one or more of Examples 79 or 88, wherein the charging station controller is configured to solve the optimal control problem using a direct collocation approach.

[0324] Example 92 is the subject matter of any one or more of Examples 79, 88, or 91, wherein the direct collocation approach discretizes the charging process into intervals and approximates state trajectories using polynomial functions.

[0325] Example 93 is the subject matter of any one or more of Examples 79, 88, 91-92, wherein the polynomial functions are third-order Lagrange polynomials with Legendre- Radau collocation points.

[0326] Example 94 is the subject matter of Example 79, wherein the charging station controller is configured to use a lookup table that correlates cell temperature and terminal voltage with permissible charging currents.

[0327] Example 95 is the subject matter of any one or more of Examples 79 or 94, wherein the lookup table is derived using a pseudo-2D electrochemical model.

[0328] Example 96 is the subject matter of any one or more of Examples 79, 94-95, wherein the pseudo-2D electrochemical model is implemented with a PI controller to ensure a plating overpotential remains above a minimum threshold.

[0329] Example 97 is the subject matter of Example 79, wherein the charging station controller is configured to implement a voltage-based current limiting strategy.

[0330] Example 98 is the subject matter of Example 79, wherein the charging station controller is configured to receive battery data from an energy management system of the electric vehicle and use the received data to refine the coupled electro-thermal model.

Claims

CLAIMS What is claimed is:

1. A method for managing a charging process of a battery pack for an electric vehicle, comprising: initiating a charging cycle for the battery pack; actively managing temperature of the battery pack during the charging cycle by flowing a temperature-controlled coolant through a thermal management system; monitoring state-of-charge (SOC) and temperature of the battery pack; adjusting a charging current and a coolant temperature of the temperature-controlled coolant based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation; and terminating the charging cycle based on the target SOC and the target temperature range being achieved within predefined constraints.

2. The method of claim 1, wherein the charging current is adjusted based on a constraint derived from an electrochemical model of the battery pack to prevent degradation mechanisms.

3. The method of claim 2, wherein the constraint is determined by maintaining a specific overpotential within battery cells of the battery pack above a predetermined threshold.

4. The method of claim 1, wherein the temperature-controlled coolant is provided by an external cooling system capable of adjusting a coolant flow rate based on the temperature of the battery pack.

5. The method of claim 4, wherein the external cooling system includes a temperature adjustment mechanism capable of changing the coolant temperature by at least 5°C within 60 seconds to meet the target temperature range.

6. The method of claim 1, wherein the coupled electro-thermal model includes a thermal network model that simulates temperature distribution within the battery pack.

7. The method of claim 6, wherein the thermal network model comprises a plurality of nodes that represent respective regions within the battery pack.

8. The method of claim 1, wherein the adjusting of the charging current and the coolant temperature are based on an optimization algorithm that reduces charging time.

9. The method of any one of claims 1-8, wherein the charging cycle includes a plurality of stages with varying charging currents to control the charging process, and wherein the stages are dynamically adjusted based on real-time SOC and temperature data from the battery pack.

10. A system for managing a charging process of a battery pack for an electric vehicle, comprising: a thermal management system configured to flow a temperature-controlled coolant; a charging station controller configured to: initiate a charging cycle for the battery pack; monitor state-of-charge (SOC) and temperature of the battery pack; adjust a charging current and a coolant temperature of the temperature-controlled coolant based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation; and terminate the charging cycle based on the target SOC and the target temperature range being achieved within predefined constraints.

11. The system of claim 10, wherein the charging station controller is further configured to adjust the charging current based on a constraint derived from an electrochemical model of the battery pack to prevent degradation mechanisms.

12. The system of claim 11, wherein the constraint is determined by maintaining a specific overpotential within battery cells of the battery pack above a predetermined threshold.

13. The system of claim 10, wherein the thermal management system comprises an external cooling system capable of adjusting a coolant flow rate based on the temperature of the battery pack.

14. The system of claim 13, wherein the external cooling system includes a temperature adjustment mechanism capable of changing the coolant temperature by at least 5°C within 60 seconds to meet the target temperature range.

15. The system of any one of claims 10-14, wherein the coupled electro-thermal model includes a thermal network model that simulates temperature distribution within the battery pack, the thermal network model comprising a plurality of nodes that represent respective regions within the battery pack.

Images