Regenerative Braking Control for EV Battery Temperature
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Solution Overview
Problem
Lithium ion batteries in electric vehicles face performance and lifespan degradation due to temperature increases during operation, which affects energy capture efficiency and lifespan, and existing management systems fail to effectively control these operational parameters.
Innovation Solution
A battery system with a control module that predicts driving patterns and battery resistance, and adjusts the temperature trajectory of lithium ion batteries by controlling the regenerative braking system to maintain optimal operating conditions, using a combination of predictive and reactive control schemes to de-rate and re-rate the battery system based on temperature thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the battery system operates at high power during regenerative braking, then energy capture efficiency is improved, but battery temperature increases causing performance degradation
Solution Approach 1:
The control module predicts future driving patterns and battery temperature trajectories in advance, allowing the system to proactively adjust operational parameters before excessive temperature rise occurs. This predictive approach enables the battery to operate at optimal power levels during regenerative braking while preventing temperature-induced performance degradation.
Solution Approach 2:
The system dynamically adjusts the battery's operational state by switching between different control modes (predictive control when temperature is acceptable, reactive control when temperature exceeds thresholds). This dynamic adaptation allows the system to maximize energy capture during favorable conditions while preventing thermal damage during high-stress operations.
2Temperature
If additional cooling systems are added to manage battery temperature, then temperature control is improved, but device complexity increases
Solution Approach 1:
The battery management system uses the battery's own operational characteristics (current draw, voltage, state of charge) to self-regulate temperature through predictive and reactive control algorithms. The system leverages existing operational data to anticipate temperature issues and adjust charging/discharging rates, eliminating the need for separate active cooling mechanisms.
Solution Approach 2:
The patent replaces mechanical cooling systems with a control-based thermal management approach. Instead of using physical cooling components (fans, liquid cooling loops, heat sinks), the system uses software algorithms to manage temperature by controlling electrical operational parameters, thereby reducing mechanical complexity.
3Power
If the battery system is operated without de-rating, then power output is maintained, but lifespan decreases due to temperature stress
Solution Approach 1:
The control module dynamically changes operational parameters (current limits, voltage thresholds, state of charge targets) based on predicted temperature trajectories and driving patterns. By adjusting these parameters proactively, the system maintains power output within safe thermal boundaries, preventing lifespan reduction while maximizing energy utilization.
Solution Approach 2:
The system continuously monitors battery temperature, current, and voltage, using this feedback to adjust operational limits in real-time. The predictive control algorithm incorporates feedback from previous driving patterns and thermal responses to optimize future operational parameters, ensuring power output is maintained without exceeding thermal thresholds that would reduce lifespan.
Data Source
AI summary
Embodiments describe a battery system that includes a first battery module coupled to a regenerative braking system and a control module that controls operation of the battery system by: determining a predicted driving pattern over a prediction horizon using a driving pattern recognition model based in part on a battery current and a previous driving pattern; determining a predicted battery resistance of the first battery module over the prediction horizon using a recursive battery model based in part on the predicted driving pattern, the battery current, a present bus voltage, and a previous bus voltage; determining a target trajectory of a battery temperature of the first battery module over a control horizon using an objective function; and controlling magnitude and duration of electrical power supplied from the regenerative such that a predicted trajectory of the battery temperature is guided toward the target trajectory of the battery temperature during the control horizon.