Cascaded Energy Module Balancing Using Status-Based PWM Control
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Solution Overview
Problem
Existing energy systems in automobiles and other applications lack the ability to monitor individual cell health, adjust power draw per cell, optimize charging flows, and control next-generation motors effectively, leading to reduced battery performance, reliability, and inefficient energy management.
Innovation Solution
Implementing a control system that generates a module status value representative of operating characteristics like state of charge and temperature, using thresholds to balance module operation, and employing modulation indices for pulse width modulation to achieve intraphase and interphase balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional battery management systems are used with simple embedded BMS in each module, then device complexity is reduced and ease of manufacture is improved, but measurement precision of individual cell health and inability to adjust power draw per cell results in reduced battery performance and reliability
Solution Approach 1:
The battery pack is divided into multiple modules, each with its own control circuit that independently monitors and controls individual cells. This segmentation allows precise measurement and control of each cell's state of charge, temperature, and health without requiring a monolithic complex system, thereby improving reliability while managing complexity through modular architecture.
Solution Approach 2:
The control system continuously monitors individual cell parameters (voltage, temperature, current) and provides real-time feedback to adjust power draw and charging currents per cell. This feedback mechanism enables dynamic optimization of battery performance and reliability by preventing overcharge, overheating, and imbalanced cell operation.
2Productivity
If steady constant charging is applied to the battery pack, then charging system simplicity is maintained, but charging efficiency is reduced and loss of time increases due to inability to implement optimized charging techniques
Solution Approach 1:
The control system implements pulsed charging techniques by periodically varying charging current magnitude and direction based on real-time cell state measurements. This periodic action allows the battery to accept charge more efficiently during optimal windows while preventing overheating and gas generation, thereby reducing total charging time and improving charging efficiency compared to steady constant charging.
Solution Approach 2:
The charging system dynamically adjusts charging parameters (current magnitude, voltage, pulse duration) based on real-time monitoring of cell state of charge, temperature, and health. This dynamic control enables optimized charging profiles that adapt to changing battery conditions, maximizing charging efficiency and reducing charging time while maintaining safety.
3Loss of energy
If regenerative braking power is dissipated via dump resistor, then energy loss occurs and use of energy deteriorates, but device complexity is avoided compared to systems with energy recovery capability
Solution Approach 1:
The control system captures energy that would otherwise be dissipated as heat in dump resistors by redirecting regenerative braking energy to charge battery cells that are not currently being charged or are at lower state of charge. This converts the harmful energy loss into a beneficial charging opportunity, improving overall energy efficiency while using existing battery management infrastructure.
Solution Approach 2:
The system recovers energy during regenerative braking by directing current to appropriate battery modules instead of dissipating it. The control algorithm identifies which cells can accept charge and routes regenerative energy accordingly, effectively recovering what would otherwise be wasted energy and improving overall system efficiency.
4Reliability
If the weakest cell constrains the overall battery pack performance, then battery reliability is improved by ensuring all cells operate within safe limits, but productivity and use of energy are reduced due to inability to utilize full battery capacity
Solution Approach 1:
The control system applies different current magnitudes and charging parameters to individual cells or modules based on their specific state of charge, temperature, and health. This local quality approach allows the weakest cells to be protected while stronger cells can operate at higher utilization, thereby maintaining safety while maximizing overall battery pack performance and energy utilization.
Solution Approach 2:
The control algorithm dynamically changes operating parameters (current magnitude, voltage, pulse duration) for each cell or module based on real-time measurements of state of charge, temperature, and health. This parameter adaptation allows the system to optimize performance for each cell's capabilities while ensuring the weakest cells remain within safe operating limits, thus balancing reliability and productivity.
Data Source
AI summary
Example embodiments of systems, devices, and methods are provided for intraphase and interphase balancing of modular energy systems. The embodiments can be used in a broad variety of mobile and stationary applications in a broad variety of modular cascaded topologies. The embodiments can include the generation of a module status value that is representative of status information collected or determined for the module. The module status value can be an intermediate quantitative representation of the status of each module as it pertains to one or more operating characteristics sought to be balanced by the system. This intermediate quantitative representation can then be used in the generation of a modulation index for the module, which can then be used as part of a larger control technique, such as pulse width modulation, for control and balancing of the system.